Cloud Data Loss Prevention (Cloud DLP) ora fa parte di Sensitive Data Protection. Il nome dell'API rimane invariato: API Cloud Data Loss Prevention (API DLP). Per informazioni sui servizi che compongono Sensitive Data Protection, consulta la panoramica di Sensitive Data Protection.

Questa pagina è stata tradotta dall'API Cloud Translation.

Calcolo di k-anonymity per un set di dati

K-anonymity è una proprietà di un set di dati che indica la reidentificabilità dei suoi record. Un set di dati è k-anonymous se i quasi-identificatori per ogni persona nel set di dati sono identici ad almeno k - 1 altre persone presenti nel set di dati.

Puoi calcolare il valore di k-anonimità in base a una o più colonne o campi di un set di dati. Questo argomento mostra come calcolare i valori di k-anonimità per un set di dati utilizzando Sensitive Data Protection. Per ulteriori informazioni sull'anonimizzazione k o sull'analisi del rischio in generale, consulta l'argomento relativo al concetto di analisi del rischio prima di continuare.

Prima di iniziare

Prima di continuare, assicurati di aver eseguito quanto segue:

Accedi al tuo Account Google.
Nella console Google Cloud, nella pagina del selettore di progetti, seleziona o crea un progetto Google Cloud.

Vai al selettore dei progetti

Verifica che la fatturazione sia attivata per il tuo progetto Google Cloud. Scopri come verificare che la fatturazione sia attivata per il tuo progetto.
Attiva Sensitive Data Protection.

Abilita Sensitive Data Protection

Seleziona un set di dati BigQuery da analizzare. La funzionalità Protezione dei dati sensibili calcola la metrica di anonimizzazione k eseguendo la scansione di una tabella BigQuery.
Determina un identificatore (se applicabile) e almeno un quasi-identificatore nel set di dati. Per ulteriori informazioni, consulta Termini e tecniche di analisi del rischio.

Calcolare k-anonymity

Sensitive Data Protection esegue l'analisi del rischio ogni volta che viene eseguito un job di analisi del rischio. Devi prima creare il job utilizzando la console Google Cloud, inviando una richiesta all'API DLP o utilizzando una libreria client Sensitive Data Protection.

Console

Nella console Google Cloud, vai alla pagina Crea analisi dei rischi.

Vai a Crea analisi del rischio
Nella sezione Scegli i dati di input, specifica la tabella BigQuery da analizzare inserendo l'ID progetto del progetto contenente la tabella, l'ID set di dati della tabella e il nome della tabella.
In Metrica di privacy da calcolare, seleziona Anonymity k.
Nella sezione ID job, puoi facoltativamente assegnare al job un identificatore personalizzato e selezionare una posizione della risorsa in cui Sensitive Data Protection elaborerà i tuoi dati. Al termine, fai clic su Continua.
Nella sezione Definisci campi, specifica identificatori e quasi-identificatori per il job relativo al rischio di anonimato k. Sensitive Data Protection accede ai metadati della tabella BigQuery specificata nel passaggio precedente e tenta di compilare l'elenco dei campi.
1. Seleziona la casella di controllo appropriata per specificare un campo come identificatore (ID) o quasi-identificatore (QI). Devi selezionare 0 o 1 identificatori e almeno 1 quasi-identificatore.
2. Se la funzionalità Protezione dei dati sensibili non è in grado di compilare i campi, fai clic su Inserisci il nome del campo per inserire manualmente uno o più campi e impostarli ciascuno come identificativo o quasi-identificatore. Al termine, fai clic su Continua.
Nella sezione Aggiungi azioni, puoi aggiungere azioni facoltative da eseguire al termine del job di rischio. Le opzioni disponibili sono:

Importante: tieni presente che l'esecuzione di un job di analisi dei rischi potrebbe mantenere alcuni dei valori di quote dei quasi-identificatori selezionati nei risultati del job di analisi dei rischi. Questi valori delle quote vengono utilizzati per creare l'istogramma e le tabelle di output.
- Salva in BigQuery: salva i risultati della scansione di analisi del rischio in una tabella BigQuery.
- Pubblica in Pub/Sub: pubblica una notifica in un argomento Pub/Sub.
  
  Nota: se si verificano problemi di configurazione o autorizzazione con l'argomento Pub/Sub, la Protezione dei dati sensibili riprova a inviare la notifica Pub/Sub per un massimo di due settimane. Dopo due settimane, la notifica viene eliminata.
- Invia una notifica via email: ti invia un'email con i risultati. Al termine, fai clic su Crea.

Il job di analisi del rischio di k-anonimizzazione si avvia immediatamente.

C#

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.

Per autenticarti in Sensitive Data Protection, configura le Credenziali predefinite dell'applicazione. Per maggiori informazioni, consulta Configurare l'autenticazione per un ambiente di sviluppo locale.


using Google.Api.Gax.ResourceNames;
using Google.Cloud.Dlp.V2;
using Google.Cloud.PubSub.V1;
using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;
using static Google.Cloud.Dlp.V2.Action.Types;
using static Google.Cloud.Dlp.V2.PrivacyMetric.Types;

public class RiskAnalysisCreateKAnonymity
{
    public static AnalyzeDataSourceRiskDetails.Types.KAnonymityResult KAnonymity(
        string callingProjectId,
        string tableProjectId,
        string datasetId,
        string tableId,
        string topicId,
        string subscriptionId,
        IEnumerable<FieldId> quasiIds)
    {
        var dlp = DlpServiceClient.Create();

        // Construct + submit the job
        var KAnonymityConfig = new KAnonymityConfig
        {
            QuasiIds = { quasiIds }
        };

        var config = new RiskAnalysisJobConfig
        {
            PrivacyMetric = new PrivacyMetric
            {
                KAnonymityConfig = KAnonymityConfig
            },
            SourceTable = new BigQueryTable
            {
                ProjectId = tableProjectId,
                DatasetId = datasetId,
                TableId = tableId
            },
            Actions =
            {
                new Google.Cloud.Dlp.V2.Action
                {
                    PubSub = new PublishToPubSub
                    {
                        Topic = $"projects/{callingProjectId}/topics/{topicId}"
                    }
                }
            }
        };

        var submittedJob = dlp.CreateDlpJob(
            new CreateDlpJobRequest
            {
                ParentAsProjectName = new ProjectName(callingProjectId),
                RiskJob = config
            });

        // Listen to pub/sub for the job
        var subscriptionName = new SubscriptionName(callingProjectId, subscriptionId);
        var subscriber = SubscriberClient.CreateAsync(
            subscriptionName).Result;

        // SimpleSubscriber runs your message handle function on multiple
        // threads to maximize throughput.
        var done = new ManualResetEventSlim(false);
        subscriber.StartAsync((PubsubMessage message, CancellationToken cancel) =>
        {
            if (message.Attributes["DlpJobName"] == submittedJob.Name)
            {
                Thread.Sleep(500); // Wait for DLP API results to become consistent
                done.Set();
                return Task.FromResult(SubscriberClient.Reply.Ack);
            }
            else
            {
                return Task.FromResult(SubscriberClient.Reply.Nack);
            }
        });

        done.Wait(TimeSpan.FromMinutes(10)); // 10 minute timeout; may not work for large jobs
        subscriber.StopAsync(CancellationToken.None).Wait();

        // Process results
        var resultJob = dlp.GetDlpJob(new GetDlpJobRequest
        {
            DlpJobName = DlpJobName.Parse(submittedJob.Name)
        });

        var result = resultJob.RiskDetails.KAnonymityResult;

        for (var bucketIdx = 0; bucketIdx < result.EquivalenceClassHistogramBuckets.Count; bucketIdx++)
        {
            var bucket = result.EquivalenceClassHistogramBuckets[bucketIdx];
            Console.WriteLine($"Bucket {bucketIdx}");
            Console.WriteLine($"  Bucket size range: [{bucket.EquivalenceClassSizeLowerBound}, {bucket.EquivalenceClassSizeUpperBound}].");
            Console.WriteLine($"  {bucket.BucketSize} unique value(s) total.");

            foreach (var bucketValue in bucket.BucketValues)
            {
                // 'UnpackValue(x)' is a prettier version of 'x.toString()'
                Console.WriteLine($"    Quasi-ID values: [{String.Join(',', bucketValue.QuasiIdsValues.Select(x => UnpackValue(x)))}]");
                Console.WriteLine($"    Class size: {bucketValue.EquivalenceClassSize}");
            }
        }

        return result;
    }

    public static string UnpackValue(Value protoValue)
    {
        var jsonValue = JsonConvert.DeserializeObject<Dictionary<string, object>>(protoValue.ToString());
        return jsonValue.Values.ElementAt(0).ToString();
    }
}

Go

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.

import (
	"context"
	"fmt"
	"io"
	"strings"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
	"cloud.google.com/go/pubsub"
)

// riskKAnonymity computes the risk of the given columns using K Anonymity.
func riskKAnonymity(w io.Writer, projectID, dataProject, pubSubTopic, pubSubSub, datasetID, tableID string, columnNames ...string) error {
	// projectID := "my-project-id"
	// dataProject := "bigquery-public-data"
	// pubSubTopic := "dlp-risk-sample-topic"
	// pubSubSub := "dlp-risk-sample-sub"
	// datasetID := "nhtsa_traffic_fatalities"
	// tableID := "accident_2015"
	// columnNames := "state_number" "county"
	ctx := context.Background()
	client, err := dlp.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("dlp.NewClient: %w", err)
	}

	// Create a PubSub Client used to listen for when the inspect job finishes.
	pubsubClient, err := pubsub.NewClient(ctx, projectID)
	if err != nil {
		return err
	}
	defer pubsubClient.Close()

	// Create a PubSub subscription we can use to listen for messages.
	// Create the Topic if it doesn't exist.
	t := pubsubClient.Topic(pubSubTopic)
	topicExists, err := t.Exists(ctx)
	if err != nil {
		return err
	}
	if !topicExists {
		if t, err = pubsubClient.CreateTopic(ctx, pubSubTopic); err != nil {
			return err
		}
	}

	// Create the Subscription if it doesn't exist.
	s := pubsubClient.Subscription(pubSubSub)
	subExists, err := s.Exists(ctx)
	if err != nil {
		return err
	}
	if !subExists {
		if s, err = pubsubClient.CreateSubscription(ctx, pubSubSub, pubsub.SubscriptionConfig{Topic: t}); err != nil {
			return err
		}
	}

	// topic is the PubSub topic string where messages should be sent.
	topic := "projects/" + projectID + "/topics/" + pubSubTopic

	// Build the QuasiID slice.
	var q []*dlppb.FieldId
	for _, c := range columnNames {
		q = append(q, &dlppb.FieldId{Name: c})
	}

	// Create a configured request.
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_RiskJob{
			RiskJob: &dlppb.RiskAnalysisJobConfig{
				// PrivacyMetric configures what to compute.
				PrivacyMetric: &dlppb.PrivacyMetric{
					Type: &dlppb.PrivacyMetric_KAnonymityConfig_{
						KAnonymityConfig: &dlppb.PrivacyMetric_KAnonymityConfig{
							QuasiIds: q,
						},
					},
				},
				// SourceTable describes where to find the data.
				SourceTable: &dlppb.BigQueryTable{
					ProjectId: dataProject,
					DatasetId: datasetID,
					TableId:   tableID,
				},
				// Send a message to PubSub using Actions.
				Actions: []*dlppb.Action{
					{
						Action: &dlppb.Action_PubSub{
							PubSub: &dlppb.Action_PublishToPubSub{
								Topic: topic,
							},
						},
					},
				},
			},
		},
	}
	// Create the risk job.
	j, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateDlpJob: %w", err)
	}
	fmt.Fprintf(w, "Created job: %v\n", j.GetName())

	// Wait for the risk job to finish by waiting for a PubSub message.
	// This only waits for 10 minutes. For long jobs, consider using a truly
	// asynchronous execution model such as Cloud Functions.
	ctx, cancel := context.WithTimeout(ctx, 10*time.Minute)
	defer cancel()
	err = s.Receive(ctx, func(ctx context.Context, msg *pubsub.Message) {
		// If this is the wrong job, do not process the result.
		if msg.Attributes["DlpJobName"] != j.GetName() {
			msg.Nack()
			return
		}
		msg.Ack()
		time.Sleep(500 * time.Millisecond)
		j, err := client.GetDlpJob(ctx, &dlppb.GetDlpJobRequest{
			Name: j.GetName(),
		})
		if err != nil {
			fmt.Fprintf(w, "GetDlpJob: %v", err)
			return
		}
		h := j.GetRiskDetails().GetKAnonymityResult().GetEquivalenceClassHistogramBuckets()
		for i, b := range h {
			fmt.Fprintf(w, "Histogram bucket %v\n", i)
			fmt.Fprintf(w, "  Size range: [%v,%v]\n", b.GetEquivalenceClassSizeLowerBound(), b.GetEquivalenceClassSizeUpperBound())
			fmt.Fprintf(w, "  %v unique values total\n", b.GetBucketSize())
			for _, v := range b.GetBucketValues() {
				var qvs []string
				for _, qv := range v.GetQuasiIdsValues() {
					qvs = append(qvs, qv.String())
				}
				fmt.Fprintf(w, "    QuasiID values: %s\n", strings.Join(qvs, ", "))
				fmt.Fprintf(w, "    Class size: %v\n", v.GetEquivalenceClassSize())
			}
		}
		// Stop listening for more messages.
		cancel()
	})
	if err != nil {
		return fmt.Errorf("Receive: %w", err)
	}
	return nil
}

Java

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.


import com.google.api.core.SettableApiFuture;
import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.cloud.pubsub.v1.AckReplyConsumer;
import com.google.cloud.pubsub.v1.MessageReceiver;
import com.google.cloud.pubsub.v1.Subscriber;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.Action.PublishToPubSub;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult.KAnonymityEquivalenceClass;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult.KAnonymityHistogramBucket;
import com.google.privacy.dlp.v2.BigQueryTable;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.FieldId;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.PrivacyMetric;
import com.google.privacy.dlp.v2.PrivacyMetric.KAnonymityConfig;
import com.google.privacy.dlp.v2.RiskAnalysisJobConfig;
import com.google.privacy.dlp.v2.Value;
import com.google.pubsub.v1.ProjectSubscriptionName;
import com.google.pubsub.v1.ProjectTopicName;
import com.google.pubsub.v1.PubsubMessage;
import java.io.IOException;
import java.util.Arrays;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import java.util.stream.Collectors;

@SuppressWarnings("checkstyle:AbbreviationAsWordInName")
class RiskAnalysisKAnonymity {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String datasetId = "your-bigquery-dataset-id";
    String tableId = "your-bigquery-table-id";
    String topicId = "pub-sub-topic";
    String subscriptionId = "pub-sub-subscription";
    calculateKAnonymity(projectId, datasetId, tableId, topicId, subscriptionId);
  }

  public static void calculateKAnonymity(
      String projectId, String datasetId, String tableId, String topicId, String subscriptionId)
      throws ExecutionException, InterruptedException, IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (DlpServiceClient dlpServiceClient = DlpServiceClient.create()) {

      // Specify the BigQuery table to analyze
      BigQueryTable bigQueryTable =
          BigQueryTable.newBuilder()
              .setProjectId(projectId)
              .setDatasetId(datasetId)
              .setTableId(tableId)
              .build();

      // These values represent the column names of quasi-identifiers to analyze
      List<String> quasiIds = Arrays.asList("Age", "Mystery");

      // Configure the privacy metric for the job
      List<FieldId> quasiIdFields =
          quasiIds.stream()
              .map(columnName -> FieldId.newBuilder().setName(columnName).build())
              .collect(Collectors.toList());
      KAnonymityConfig kanonymityConfig =
          KAnonymityConfig.newBuilder().addAllQuasiIds(quasiIdFields).build();
      PrivacyMetric privacyMetric =
          PrivacyMetric.newBuilder().setKAnonymityConfig(kanonymityConfig).build();

      // Create action to publish job status notifications over Google Cloud Pub/Sub
      ProjectTopicName topicName = ProjectTopicName.of(projectId, topicId);
      PublishToPubSub publishToPubSub =
          PublishToPubSub.newBuilder().setTopic(topicName.toString()).build();
      Action action = Action.newBuilder().setPubSub(publishToPubSub).build();

      // Configure the risk analysis job to perform
      RiskAnalysisJobConfig riskAnalysisJobConfig =
          RiskAnalysisJobConfig.newBuilder()
              .setSourceTable(bigQueryTable)
              .setPrivacyMetric(privacyMetric)
              .addActions(action)
              .build();

      // Build the request to be sent by the client
      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setRiskJob(riskAnalysisJobConfig)
              .build();

      // Send the request to the API using the client
      DlpJob dlpJob = dlpServiceClient.createDlpJob(createDlpJobRequest);

      // Set up a Pub/Sub subscriber to listen on the job completion status
      final SettableApiFuture<Boolean> done = SettableApiFuture.create();

      ProjectSubscriptionName subscriptionName =
          ProjectSubscriptionName.of(projectId, subscriptionId);

      MessageReceiver messageHandler =
          (PubsubMessage pubsubMessage, AckReplyConsumer ackReplyConsumer) -> {
            handleMessage(dlpJob, done, pubsubMessage, ackReplyConsumer);
          };
      Subscriber subscriber = Subscriber.newBuilder(subscriptionName, messageHandler).build();
      subscriber.startAsync();

      // Wait for job completion semi-synchronously
      // For long jobs, consider using a truly asynchronous execution model such as Cloud Functions
      try {
        done.get(15, TimeUnit.MINUTES);
      } catch (TimeoutException e) {
        System.out.println("Job was not completed after 15 minutes.");
        return;
      } finally {
        subscriber.stopAsync();
        subscriber.awaitTerminated();
      }

      // Build a request to get the completed job
      GetDlpJobRequest getDlpJobRequest =
          GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();

      // Retrieve completed job status
      DlpJob completedJob = dlpServiceClient.getDlpJob(getDlpJobRequest);
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());

      // Get the result and parse through and process the information
      KAnonymityResult kanonymityResult = completedJob.getRiskDetails().getKAnonymityResult();
      List<KAnonymityHistogramBucket> histogramBucketList =
          kanonymityResult.getEquivalenceClassHistogramBucketsList();
      for (KAnonymityHistogramBucket result : histogramBucketList) {
        System.out.printf(
            "Bucket size range: [%d, %d]\n",
            result.getEquivalenceClassSizeLowerBound(), result.getEquivalenceClassSizeUpperBound());

        for (KAnonymityEquivalenceClass bucket : result.getBucketValuesList()) {
          List<String> quasiIdValues =
              bucket.getQuasiIdsValuesList().stream()
                  .map(Value::toString)
                  .collect(Collectors.toList());

          System.out.println("\tQuasi-ID values: " + String.join(", ", quasiIdValues));
          System.out.println("\tClass size: " + bucket.getEquivalenceClassSize());
        }
      }
    }
  }

  // handleMessage injects the job and settableFuture into the message reciever interface
  private static void handleMessage(
      DlpJob job,
      SettableApiFuture<Boolean> done,
      PubsubMessage pubsubMessage,
      AckReplyConsumer ackReplyConsumer) {
    String messageAttribute = pubsubMessage.getAttributesMap().get("DlpJobName");
    if (job.getName().equals(messageAttribute)) {
      done.set(true);
      ackReplyConsumer.ack();
    } else {
      ackReplyConsumer.nack();
    }
  }
}

Node.js

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.

// Import the Google Cloud client libraries
const DLP = require('@google-cloud/dlp');
const {PubSub} = require('@google-cloud/pubsub');

// Instantiates clients
const dlp = new DLP.DlpServiceClient();
const pubsub = new PubSub();

// The project ID to run the API call under
// const projectId = 'my-project';

// The project ID the table is stored under
// This may or (for public datasets) may not equal the calling project ID
// const tableProjectId = 'my-project';

// The ID of the dataset to inspect, e.g. 'my_dataset'
// const datasetId = 'my_dataset';

// The ID of the table to inspect, e.g. 'my_table'
// const tableId = 'my_table';

// The name of the Pub/Sub topic to notify once the job completes
// TODO(developer): create a Pub/Sub topic to use for this
// const topicId = 'MY-PUBSUB-TOPIC'

// The name of the Pub/Sub subscription to use when listening for job
// completion notifications
// TODO(developer): create a Pub/Sub subscription to use for this
// const subscriptionId = 'MY-PUBSUB-SUBSCRIPTION'

// A set of columns that form a composite key ('quasi-identifiers')
// const quasiIds = [{ name: 'age' }, { name: 'city' }];
async function kAnonymityAnalysis() {
  const sourceTable = {
    projectId: tableProjectId,
    datasetId: datasetId,
    tableId: tableId,
  };
  // Construct request for creating a risk analysis job

  const request = {
    parent: `projects/${projectId}/locations/global`,
    riskJob: {
      privacyMetric: {
        kAnonymityConfig: {
          quasiIds: quasiIds,
        },
      },
      sourceTable: sourceTable,
      actions: [
        {
          pubSub: {
            topic: `projects/${projectId}/topics/${topicId}`,
          },
        },
      ],
    },
  };

  // Create helper function for unpacking values
  const getValue = obj => obj[Object.keys(obj)[0]];

  // Run risk analysis job
  const [topicResponse] = await pubsub.topic(topicId).get();
  const subscription = await topicResponse.subscription(subscriptionId);
  const [jobsResponse] = await dlp.createDlpJob(request);
  const jobName = jobsResponse.name;
  console.log(`Job created. Job name: ${jobName}`);
  // Watch the Pub/Sub topic until the DLP job finishes
  await new Promise((resolve, reject) => {
    const messageHandler = message => {
      if (message.attributes && message.attributes.DlpJobName === jobName) {
        message.ack();
        subscription.removeListener('message', messageHandler);
        subscription.removeListener('error', errorHandler);
        resolve(jobName);
      } else {
        message.nack();
      }
    };

    const errorHandler = err => {
      subscription.removeListener('message', messageHandler);
      subscription.removeListener('error', errorHandler);
      reject(err);
    };

    subscription.on('message', messageHandler);
    subscription.on('error', errorHandler);
  });
  setTimeout(() => {
    console.log(' Waiting for DLP job to fully complete');
  }, 500);
  const [job] = await dlp.getDlpJob({name: jobName});
  const histogramBuckets =
    job.riskDetails.kAnonymityResult.equivalenceClassHistogramBuckets;

  histogramBuckets.forEach((histogramBucket, histogramBucketIdx) => {
    console.log(`Bucket ${histogramBucketIdx}:`);
    console.log(
      `  Bucket size range: [${histogramBucket.equivalenceClassSizeLowerBound}, ${histogramBucket.equivalenceClassSizeUpperBound}]`
    );

    histogramBucket.bucketValues.forEach(valueBucket => {
      const quasiIdValues = valueBucket.quasiIdsValues
        .map(getValue)
        .join(', ');
      console.log(`  Quasi-ID values: {${quasiIdValues}}`);
      console.log(`  Class size: ${valueBucket.equivalenceClassSize}`);
    });
  });
}
await kAnonymityAnalysis();

PHP

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.

use Google\Cloud\Dlp\V2\RiskAnalysisJobConfig;
use Google\Cloud\Dlp\V2\BigQueryTable;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\PrivacyMetric;
use Google\Cloud\Dlp\V2\PrivacyMetric\KAnonymityConfig;
use Google\Cloud\PubSub\PubSubClient;

/**
 * Computes the k-anonymity of a column set in a Google BigQuery table.
 *
 * @param string    $callingProjectId  The project ID to run the API call under
 * @param string    $dataProjectId     The project ID containing the target Datastore
 * @param string    $topicId           The name of the Pub/Sub topic to notify once the job completes
 * @param string    $subscriptionId    The name of the Pub/Sub subscription to use when listening for job
 * @param string    $datasetId         The ID of the dataset to inspect
 * @param string    $tableId           The ID of the table to inspect
 * @param string[]  $quasiIdNames      Array columns that form a composite key (quasi-identifiers)
 */
function k_anonymity(
    string $callingProjectId,
    string $dataProjectId,
    string $topicId,
    string $subscriptionId,
    string $datasetId,
    string $tableId,
    array $quasiIdNames
): void {
    // Instantiate a client.
    $dlp = new DlpServiceClient();
    $pubsub = new PubSubClient();
    $topic = $pubsub->topic($topicId);

    // Construct risk analysis config
    $quasiIds = array_map(
        function ($id) {
            return (new FieldId())->setName($id);
        },
        $quasiIdNames
    );

    $statsConfig = (new KAnonymityConfig())
        ->setQuasiIds($quasiIds);

    $privacyMetric = (new PrivacyMetric())
        ->setKAnonymityConfig($statsConfig);

    // Construct items to be analyzed
    $bigqueryTable = (new BigQueryTable())
        ->setProjectId($dataProjectId)
        ->setDatasetId($datasetId)
        ->setTableId($tableId);

    // Construct the action to run when job completes
    $pubSubAction = (new PublishToPubSub())
        ->setTopic($topic->name());

    $action = (new Action())
        ->setPubSub($pubSubAction);

    // Construct risk analysis job config to run
    $riskJob = (new RiskAnalysisJobConfig())
        ->setPrivacyMetric($privacyMetric)
        ->setSourceTable($bigqueryTable)
        ->setActions([$action]);

    // Listen for job notifications via an existing topic/subscription.
    $subscription = $topic->subscription($subscriptionId);

    // Submit request
    $parent = "projects/$callingProjectId/locations/global";
    $createDlpJobRequest = (new CreateDlpJobRequest())
        ->setParent($parent)
        ->setRiskJob($riskJob);
    $job = $dlp->createDlpJob($createDlpJobRequest);

    // Poll Pub/Sub using exponential backoff until job finishes
    // Consider using an asynchronous execution model such as Cloud Functions
    $attempt = 1;
    $startTime = time();
    do {
        foreach ($subscription->pull() as $message) {
            if (
                isset($message->attributes()['DlpJobName']) &&
                $message->attributes()['DlpJobName'] === $job->getName()
            ) {
                $subscription->acknowledge($message);
                // Get the updated job. Loop to avoid race condition with DLP API.
                do {
                    $getDlpJobRequest = (new GetDlpJobRequest())
                        ->setName($job->getName());
                    $job = $dlp->getDlpJob($getDlpJobRequest);
                } while ($job->getState() == JobState::RUNNING);
                break 2; // break from parent do while
            }
        }
        print('Waiting for job to complete' . PHP_EOL);
        // Exponential backoff with max delay of 60 seconds
        sleep(min(60, pow(2, ++$attempt)));
    } while (time() - $startTime < 600); // 10 minute timeout

    // Print finding counts
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $histBuckets = $job->getRiskDetails()->getKAnonymityResult()->getEquivalenceClassHistogramBuckets();

            foreach ($histBuckets as $bucketIndex => $histBucket) {
                // Print bucket stats
                printf('Bucket %s:' . PHP_EOL, $bucketIndex);
                printf(
                    '  Bucket size range: [%s, %s]' . PHP_EOL,
                    $histBucket->getEquivalenceClassSizeLowerBound(),
                    $histBucket->getEquivalenceClassSizeUpperBound()
                );

                // Print bucket values
                foreach ($histBucket->getBucketValues() as $percent => $valueBucket) {
                    // Pretty-print quasi-ID values
                    print('  Quasi-ID values:' . PHP_EOL);
                    foreach ($valueBucket->getQuasiIdsValues() as $index => $value) {
                        print('    ' . $value->serializeToJsonString() . PHP_EOL);
                    }
                    printf(
                        '  Class size: %s' . PHP_EOL,
                        $valueBucket->getEquivalenceClassSize()
                    );
                }
            }

            break;
        case JobState::FAILED:
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            $errors = $job->getErrors();
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            print('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            print('Unexpected job state. Most likely, the job is either running or has not yet started.');
    }
}

Python

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.


import concurrent.futures

from typing import List

import google.cloud.dlp
from google.cloud.dlp_v2 import types
import google.cloud.pubsub


def k_anonymity_analysis(
    project: str,
    table_project_id: str,
    dataset_id: str,
    table_id: str,
    topic_id: str,
    subscription_id: str,
    quasi_ids: List[str],
    timeout: int = 300,
) -> None:
    """Uses the Data Loss Prevention API to compute the k-anonymity of a
        column set in a Google BigQuery table.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        table_project_id: The Google Cloud project id where the BigQuery table
            is stored.
        dataset_id: The id of the dataset to inspect.
        table_id: The id of the table to inspect.
        topic_id: The name of the Pub/Sub topic to notify once the job
            completes.
        subscription_id: The name of the Pub/Sub subscription to use when
            listening for job completion notifications.
        quasi_ids: A set of columns that form a composite key.
        timeout: The number of seconds to wait for a response from the API.

    Returns:
        None; the response from the API is printed to the terminal.
    """

    # Create helper function for unpacking values
    def get_values(obj: types.Value) -> int:
        return int(obj.integer_value)

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Convert the project id into a full resource id.
    topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
    parent = f"projects/{project}/locations/global"

    # Location info of the BigQuery table.
    source_table = {
        "project_id": table_project_id,
        "dataset_id": dataset_id,
        "table_id": table_id,
    }

    # Convert quasi id list to Protobuf type
    def map_fields(field: str) -> dict:
        return {"name": field}

    quasi_ids = map(map_fields, quasi_ids)

    # Tell the API where to send a notification when the job is complete.
    actions = [{"pub_sub": {"topic": topic}}]

    # Configure risk analysis job
    # Give the name of the numeric column to compute risk metrics for
    risk_job = {
        "privacy_metric": {"k_anonymity_config": {"quasi_ids": quasi_ids}},
        "source_table": source_table,
        "actions": actions,
    }

    # Call API to start risk analysis job
    operation = dlp.create_dlp_job(request={"parent": parent, "risk_job": risk_job})

    def callback(message: google.cloud.pubsub_v1.subscriber.message.Message) -> None:
        if message.attributes["DlpJobName"] == operation.name:
            # This is the message we're looking for, so acknowledge it.
            message.ack()

            # Now that the job is done, fetch the results and print them.
            job = dlp.get_dlp_job(request={"name": operation.name})
            print(f"Job name: {job.name}")
            histogram_buckets = (
                job.risk_details.k_anonymity_result.equivalence_class_histogram_buckets
            )
            # Print bucket stats
            for i, bucket in enumerate(histogram_buckets):
                print(f"Bucket {i}:")
                if bucket.equivalence_class_size_lower_bound:
                    print(
                        "   Bucket size range: [{}, {}]".format(
                            bucket.equivalence_class_size_lower_bound,
                            bucket.equivalence_class_size_upper_bound,
                        )
                    )
                    for value_bucket in bucket.bucket_values:
                        print(
                            "   Quasi-ID values: {}".format(
                                map(get_values, value_bucket.quasi_ids_values)
                            )
                        )
                        print(
                            "   Class size: {}".format(
                                value_bucket.equivalence_class_size
                            )
                        )
            subscription.set_result(None)
        else:
            # This is not the message we're looking for.
            message.drop()

    # Create a Pub/Sub client and find the subscription. The subscription is
    # expected to already be listening to the topic.
    subscriber = google.cloud.pubsub.SubscriberClient()
    subscription_path = subscriber.subscription_path(project, subscription_id)
    subscription = subscriber.subscribe(subscription_path, callback)

    try:
        subscription.result(timeout=timeout)
    except concurrent.futures.TimeoutError:
        print(
            "No event received before the timeout. Please verify that the "
            "subscription provided is subscribed to the topic provided."
        )
        subscription.close()

REST

Per eseguire un nuovo job di analisi del rischio per calcolare l'anonimizzazione K, invia una richiesta alla risorsa projects.dlpJobs, dove PROJECT_ID indica l'identificatore del progetto:

https://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs

La richiesta contiene un oggetto RiskAnalysisJobConfig composto da quanto segue:

Un oggetto PrivacyMetric. Qui specifichi che stai calcolando l'anonimità k includendo un oggetto KAnonymityConfig.
Un oggetto BigQueryTable. Specifica la tabella BigQuery da analizzare includendo quanto segue:
- projectId: l'ID del progetto contenente la tabella.
- datasetId: l'ID set di dati della tabella.
- tableId: il nome della tabella.
Un insieme di uno o più Action oggetti, che rappresentano le azioni da eseguire, nell'ordine specificato, al completamento del job. Ogni oggetto Action può contenere una delle seguenti azioni:
- SaveFindings Oggetto: salva i risultati della scansione di analisi dei rischi in una tabella BigQuery.
- PublishToPubSub Oggetto: pubblica una notifica in un argomento Pub/Sub.
  
  Nota: se si verificano problemi di configurazione o autorizzazione con l'argomento Pub/Sub, la Protezione dei dati sensibili riprova a inviare la notifica Pub/Sub per un massimo di due settimane. Dopo due settimane, la notifica viene eliminata.
- JobNotificationEmails object: ti invia un'email con i risultati.
All'interno dell'oggetto KAnonymityConfig specifica quanto segue:

Importante: tieni presente che l'esecuzione di un job di analisi dei rischi potrebbe mantenere alcuni dei valori di citazione dei quasi-identificatori selezionati nei risultati del job di analisi dei rischi. Questi valori delle quote vengono utilizzati per creare l'istogramma e le tabelle di output.
- quasiIds[]: uno o più quasi-identificatori (oggetti FieldId) da analizzare e utilizzare per calcolare l'anonimato k. Quando specifichi più quasi-identificatori, vengono considerati come una singola chiave composta. I tipi di dati ripetuti e di struct non sono supportati, ma i campi nidificati sono supportati a condizione che non siano struct o nidificati all'interno di un campo ripetuto.
- entityId: valore dell'identificatore facoltativo che, se impostato, indica che tutte le righe corrispondenti a ogni entityId distinto devono essere raggruppate per il calcolo dell'anonimizzazione k. In genere, un entityId sarà una colonna che rappresenta un utente univoco, ad esempio un ID cliente o un ID utente. Quando un entityId viene visualizzato in più righe con valori di quasi-identificatori diversi, queste righe vengono unite per formare un multiset che verrà utilizzato come quasi-identificatori per l'entità. Per saperne di più sugli ID entità, consulta ID entità e calcolo della k-anonimity nell'argomento concettuale Analisi del rischio.

Non appena invii una richiesta all'API DLP, viene avviato il job di analisi del rischio.

Elenca i job di analisi del rischio completati

Puoi visualizzare un elenco dei job di analisi dei rischi eseguiti nel progetto corrente.

Console

Per elencare i job di analisi dei rischi in esecuzione e quelli eseguiti in precedenza nella console Google Cloud, segui questi passaggi:

Nella console Google Cloud, apri Sensitive Data Protection.

Vai a Sensitive Data Protection
Fai clic sulla scheda Job e attivatori dei job nella parte superiore della pagina.
Fai clic sulla scheda Job a rischio.

Viene visualizzata l'offerta di lavoro con rischio.

Protocollo

Per elencare i job di analisi dei rischi in esecuzione e quelli eseguiti in precedenza, invia una richiesta GET alla risorsa projects.dlpJobs. L'aggiunta di un filtro per tipo di job (?type=RISK_ANALYSIS_JOB) restringe la risposta ai soli job di analisi del rischio.

https://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs?type=RISK_ANALYSIS_JOB

La risposta che ricevi contiene una rappresentazione JSON di tutti i job di analisi del rischio attuali e precedenti.

Visualizzare i risultati del job di k-anonymity

Sensitive Data Protection nella console Google Cloud include visualizzazioni integrate per i job di anonimizzazione k completati. Dopo aver seguito le istruzioni riportate nella sezione precedente, seleziona il job per cui vuoi visualizzare i risultati dall'elenco dei job di analisi dei rischi. Se il job è stato eseguito correttamente, la parte superiore della pagina Dettagli analisi del rischio ha il seguente aspetto:

Nella parte superiore della pagina sono riportate le informazioni sul job relativo al rischio di anonimato k, incluso il relativo ID e, in Contenitore, la posizione della risorsa.

Per visualizzare i risultati del calcolo di k-anonymity, fai clic sulla scheda K-anonymity. Per visualizzare la configurazione del job di analisi dei rischi, fai clic sulla scheda Configurazione.

La scheda Anonimizzazione K elenca innanzitutto l'ID entità (se presente) e i quasi-identificatori utilizzati per calcolare l'anonimizzazione k.

Grafico dei rischi

Il grafico Rischio di reidentificazione indica sull'asse y la potenziale percentuale di perdita di dati sia per le righe univoche sia per le combinazioni di quasi-identificatori univoci per ottenere, sull'asse x, un valore di anonimizzazione k. Anche il colore del grafico indica il potenziale rischio. Le tonalità di blu più scure indicano un rischio maggiore, mentre quelle più chiare indicano un rischio minore.

Valori di k-anonimità più elevati indicano un rischio inferiore di reidentificazione. Tuttavia, per ottenere valori di k-anonimità più elevati, devi rimuovere percentuali più elevate di righe totali e combinazioni di quasi-identificatori univoci più elevate, il che potrebbe ridurre l'utilità dei dati. Per visualizzare un valore specifico della potenziale perdita percentuale per un determinato valore di k-anonimità, passa il cursore sopra il grafico. Come mostrato nello screenshot, nel grafico viene visualizzata una descrizione comando.

Per visualizzare ulteriori dettagli su un valore k-anonymity specifico, fai clic sul corrispondente punto dati. Sotto il grafico viene visualizzata una spiegazione dettagliata e più in basso nella pagina viene visualizzata una tabella di dati di esempio.

Tabella dei dati di esempio del rischio

Il secondo componente della pagina dei risultati del job di analisi del rischio è la tabella di dati di esempio. Mostra le combinazioni di quasi-identificatori per un determinato valore di k-anonymity di destinazione.

La prima colonna della tabella elenca i valori di k-anonimità. Fai clic su un valore di k-anonimità per visualizzare i dati di esempio corrispondenti che devono essere eliminati per ottenere quel valore.

La seconda colonna mostra la rispettiva potenziale perdita di dati delle righe univoche e delle combinazioni di quasi-identificatori, nonché il numero di gruppi con almeno k record e il numero totale di record.

L'ultima colonna mostra un campione di gruppi che condividono una combinazione di quasi-identificatori, insieme al numero di record esistenti per quella combinazione.

Recuperare i dettagli del job utilizzando REST

Per recuperare i risultati del job di analisi del rischio di anonimizzazione k utilizzando l'API REST, invia la seguente richiesta GET alla risorsa projects.dlpJobs. Sostituisci PROJECT_ID con l'ID del tuo progetto e JOB_ID con l'identificatore del job per cui vuoi ottenere i risultati. L'ID job è stato restituito quando hai avviato il job e può essere recuperato anche elencando tutti i job.

GET https://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs/JOB_ID

La richiesta restituisce un oggetto JSON contenente un'istanza del job. I risultati dell'analisi si trovano all'interno della chiave "riskDetails", in un oggetto AnalyzeDataSourceRiskDetails. Per ulteriori informazioni, consulta il riferimento all'API per la risorsa DlpJob.

Esempio di codice: calcolo per k-anonymity con un ID entità

Questo esempio crea un job di analisi del rischio che calcola l'anonimizzazione k con un ID entità.

Per saperne di più sugli ID entità, consulta ID entità e calcolo dell'anonimizzazione k.

C#

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.


using System;
using System.Collections.Generic;
using System.Linq;
using Google.Api.Gax.ResourceNames;
using Google.Cloud.Dlp.V2;
using Newtonsoft.Json;

public class CalculateKAnonymityOnDataset
{
    public static DlpJob CalculateKAnonymitty(
        string projectId,
        string datasetId,
        string sourceTableId,
        string outputTableId)
    {
        // Construct the dlp client.
        var dlp = DlpServiceClient.Create();

        // Construct the k-anonymity config by setting the EntityId as user_id column
        // and two quasi-identifiers columns.
        var kAnonymity = new PrivacyMetric.Types.KAnonymityConfig
        {
            EntityId = new EntityId
            {
                Field = new FieldId { Name = "Name" }
            },
            QuasiIds =
            {
                new FieldId { Name = "Age" },
                new FieldId { Name = "Mystery" }
            }
        };

        // Construct risk analysis job config by providing the source table, privacy metric
        // and action to save the findings to a BigQuery table.
        var riskJob = new RiskAnalysisJobConfig
        {
            SourceTable = new BigQueryTable
            {
                ProjectId = projectId,
                DatasetId = datasetId,
                TableId = sourceTableId,
            },
            PrivacyMetric = new PrivacyMetric
            {
                KAnonymityConfig = kAnonymity,
            },
            Actions =
            {
                new Google.Cloud.Dlp.V2.Action
                {
                    SaveFindings = new Google.Cloud.Dlp.V2.Action.Types.SaveFindings
                    {
                        OutputConfig = new OutputStorageConfig
                        {
                            Table = new BigQueryTable
                            {
                                ProjectId = projectId,
                                DatasetId = datasetId,
                                TableId = outputTableId
                            }
                        }
                    }
                }
            }
        };

        // Construct the request by providing RiskJob object created above.
        var request = new CreateDlpJobRequest
        {
            ParentAsLocationName = new LocationName(projectId, "global"),
            RiskJob = riskJob
        };

        // Send the job request.
        DlpJob response = dlp.CreateDlpJob(request);

        Console.WriteLine($"Job created successfully. Job name: ${response.Name}");

        return response;
    }
}

Go

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.

import (
	"context"
	"fmt"
	"io"
	"strings"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
)

// Uses the Data Loss Prevention API to compute the k-anonymity of a
// column set in a Google BigQuery table.
func calculateKAnonymityWithEntityId(w io.Writer, projectID, datasetId, tableId string, columnNames ...string) error {
	// projectID := "your-project-id"
	// datasetId := "your-bigquery-dataset-id"
	// tableId := "your-bigquery-table-id"
	// columnNames := "age" "job_title"

	ctx := context.Background()

	// Initialize a client once and reuse it to send multiple requests. Clients
	// are safe to use across goroutines. When the client is no longer needed,
	// call the Close method to cleanup its resources.
	client, err := dlp.NewClient(ctx)
	if err != nil {
		return err
	}

	// Closing the client safely cleans up background resources.
	defer client.Close()

	// Specify the BigQuery table to analyze
	bigQueryTable := &dlppb.BigQueryTable{
		ProjectId: "bigquery-public-data",
		DatasetId: "samples",
		TableId:   "wikipedia",
	}

	// Configure the privacy metric for the job
	// Build the QuasiID slice.
	var q []*dlppb.FieldId
	for _, c := range columnNames {
		q = append(q, &dlppb.FieldId{Name: c})
	}

	entityId := &dlppb.EntityId{
		Field: &dlppb.FieldId{
			Name: "id",
		},
	}

	kAnonymityConfig := &dlppb.PrivacyMetric_KAnonymityConfig{
		QuasiIds: q,
		EntityId: entityId,
	}

	privacyMetric := &dlppb.PrivacyMetric{
		Type: &dlppb.PrivacyMetric_KAnonymityConfig_{
			KAnonymityConfig: kAnonymityConfig,
		},
	}

	// Specify the bigquery table to store the findings.
	// The "test_results" table in the given BigQuery dataset will be created if it doesn't
	// already exist.
	outputbigQueryTable := &dlppb.BigQueryTable{
		ProjectId: projectID,
		DatasetId: datasetId,
		TableId:   tableId,
	}

	// Create action to publish job status notifications to BigQuery table.
	outputStorageConfig := &dlppb.OutputStorageConfig{
		Type: &dlppb.OutputStorageConfig_Table{
			Table: outputbigQueryTable,
		},
	}

	findings := &dlppb.Action_SaveFindings{
		OutputConfig: outputStorageConfig,
	}

	action := &dlppb.Action{
		Action: &dlppb.Action_SaveFindings_{
			SaveFindings: findings,
		},
	}

	// Configure the risk analysis job to perform
	riskAnalysisJobConfig := &dlppb.RiskAnalysisJobConfig{
		PrivacyMetric: privacyMetric,
		SourceTable:   bigQueryTable,
		Actions: []*dlppb.Action{
			action,
		},
	}

	// Build the request to be sent by the client
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_RiskJob{
			RiskJob: riskAnalysisJobConfig,
		},
	}

	// Send the request to the API using the client
	dlpJob, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return err
	}
	fmt.Fprintf(w, "Created job: %v\n", dlpJob.GetName())

	// Build a request to get the completed job
	getDlpJobReq := &dlppb.GetDlpJobRequest{
		Name: dlpJob.Name,
	}

	timeout := 15 * time.Minute
	startTime := time.Now()

	var completedJob *dlppb.DlpJob

	// Wait for job completion
	for time.Since(startTime) <= timeout {
		completedJob, err = client.GetDlpJob(ctx, getDlpJobReq)
		if err != nil {
			return err
		}

		if completedJob.GetState() == dlppb.DlpJob_DONE {
			break
		}

		time.Sleep(30 * time.Second)

	}

	if completedJob.GetState() != dlppb.DlpJob_DONE {
		fmt.Println("Job did not complete within 15 minutes.")
	}

	// Retrieve completed job status
	fmt.Fprintf(w, "Job status: %v", completedJob.State)
	fmt.Fprintf(w, "Job name: %v", dlpJob.Name)

	// Get the result and parse through and process the information
	kanonymityResult := completedJob.GetRiskDetails().GetKAnonymityResult()

	for _, result := range kanonymityResult.GetEquivalenceClassHistogramBuckets() {
		fmt.Fprintf(w, "Bucket size range: [%d, %d]\n", result.GetEquivalenceClassSizeLowerBound(), result.GetEquivalenceClassSizeLowerBound())

		for _, bucket := range result.GetBucketValues() {
			quasiIdValues := []string{}
			for _, v := range bucket.GetQuasiIdsValues() {
				quasiIdValues = append(quasiIdValues, v.GetStringValue())
			}
			fmt.Fprintf(w, "\tQuasi-ID values: %s", strings.Join(quasiIdValues, ","))
			fmt.Fprintf(w, "\tClass size: %d", bucket.EquivalenceClassSize)
		}
	}

	return nil

}

Java

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.


import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.Action.SaveFindings;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult.KAnonymityEquivalenceClass;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult.KAnonymityHistogramBucket;
import com.google.privacy.dlp.v2.BigQueryTable;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.EntityId;
import com.google.privacy.dlp.v2.FieldId;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.OutputStorageConfig;
import com.google.privacy.dlp.v2.PrivacyMetric;
import com.google.privacy.dlp.v2.PrivacyMetric.KAnonymityConfig;
import com.google.privacy.dlp.v2.RiskAnalysisJobConfig;
import com.google.privacy.dlp.v2.Value;
import java.io.IOException;
import java.time.Duration;
import java.util.Arrays;
import java.util.List;
import java.util.concurrent.TimeUnit;
import java.util.stream.Collectors;

@SuppressWarnings("checkstyle:AbbreviationAsWordInName")
public class RiskAnalysisKAnonymityWithEntityId {

  public static void main(String[] args) throws IOException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    // The Google Cloud project id to use as a parent resource.
    String projectId = "your-project-id";
    // The BigQuery dataset id to be used and the reference table name to be inspected.
    String datasetId = "your-bigquery-dataset-id";
    String tableId = "your-bigquery-table-id";
    calculateKAnonymityWithEntityId(projectId, datasetId, tableId);
  }

  // Uses the Data Loss Prevention API to compute the k-anonymity of a column set in a Google
  // BigQuery table.
  public static void calculateKAnonymityWithEntityId(
      String projectId, String datasetId, String tableId) throws IOException, InterruptedException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (DlpServiceClient dlpServiceClient = DlpServiceClient.create()) {

      // Specify the BigQuery table to analyze
      BigQueryTable bigQueryTable =
          BigQueryTable.newBuilder()
              .setProjectId(projectId)
              .setDatasetId(datasetId)
              .setTableId(tableId)
              .build();

      // These values represent the column names of quasi-identifiers to analyze
      List<String> quasiIds = Arrays.asList("Age", "Mystery");

      // Create a list of FieldId objects based on the provided list of column names.
      List<FieldId> quasiIdFields =
          quasiIds.stream()
              .map(columnName -> FieldId.newBuilder().setName(columnName).build())
              .collect(Collectors.toList());

      // Specify the unique identifier in the source table for the k-anonymity analysis.
      FieldId uniqueIdField = FieldId.newBuilder().setName("Name").build();
      EntityId entityId = EntityId.newBuilder().setField(uniqueIdField).build();
      KAnonymityConfig kanonymityConfig = KAnonymityConfig.newBuilder()
              .addAllQuasiIds(quasiIdFields)
              .setEntityId(entityId)
              .build();

      // Configure the privacy metric to compute for re-identification risk analysis.
      PrivacyMetric privacyMetric =
          PrivacyMetric.newBuilder().setKAnonymityConfig(kanonymityConfig).build();

      // Specify the bigquery table to store the findings.
      // The "test_results" table in the given BigQuery dataset will be created if it doesn't
      // already exist.
      BigQueryTable outputbigQueryTable =
          BigQueryTable.newBuilder()
              .setProjectId(projectId)
              .setDatasetId(datasetId)
              .setTableId("test_results")
              .build();

      // Create action to publish job status notifications to BigQuery table.
      OutputStorageConfig outputStorageConfig =
          OutputStorageConfig.newBuilder().setTable(outputbigQueryTable).build();
      SaveFindings findings =
          SaveFindings.newBuilder().setOutputConfig(outputStorageConfig).build();
      Action action = Action.newBuilder().setSaveFindings(findings).build();

      // Configure the risk analysis job to perform
      RiskAnalysisJobConfig riskAnalysisJobConfig =
          RiskAnalysisJobConfig.newBuilder()
              .setSourceTable(bigQueryTable)
              .setPrivacyMetric(privacyMetric)
              .addActions(action)
              .build();

      // Build the request to be sent by the client
      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setRiskJob(riskAnalysisJobConfig)
              .build();

      // Send the request to the API using the client
      DlpJob dlpJob = dlpServiceClient.createDlpJob(createDlpJobRequest);

      // Build a request to get the completed job
      GetDlpJobRequest getDlpJobRequest =
          GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();

      DlpJob completedJob = null;
      // Wait for job completion
      try {
        Duration timeout = Duration.ofMinutes(15);
        long startTime = System.currentTimeMillis();
        do {
          completedJob = dlpServiceClient.getDlpJob(getDlpJobRequest);
          TimeUnit.SECONDS.sleep(30);
        } while (completedJob.getState() != DlpJob.JobState.DONE
            && System.currentTimeMillis() - startTime <= timeout.toMillis());
      } catch (InterruptedException e) {
        System.out.println("Job did not complete within 15 minutes.");
      }

      // Retrieve completed job status
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());

      // Get the result and parse through and process the information
      KAnonymityResult kanonymityResult = completedJob.getRiskDetails().getKAnonymityResult();
      for (KAnonymityHistogramBucket result :
          kanonymityResult.getEquivalenceClassHistogramBucketsList()) {
        System.out.printf(
            "Bucket size range: [%d, %d]\n",
            result.getEquivalenceClassSizeLowerBound(), result.getEquivalenceClassSizeUpperBound());

        for (KAnonymityEquivalenceClass bucket : result.getBucketValuesList()) {
          List<String> quasiIdValues =
              bucket.getQuasiIdsValuesList().stream()
                  .map(Value::toString)
                  .collect(Collectors.toList());

          System.out.println("\tQuasi-ID values: " + String.join(", ", quasiIdValues));
          System.out.println("\tClass size: " + bucket.getEquivalenceClassSize());
        }
      }
    }
  }
}

Node.js

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.

// Imports the Google Cloud Data Loss Prevention library
const DLP = require('@google-cloud/dlp');

// Instantiates a client
const dlp = new DLP.DlpServiceClient();

// The project ID to run the API call under.
// const projectId = "your-project-id";

// The ID of the dataset to inspect, e.g. 'my_dataset'
// const datasetId = 'my_dataset';

// The ID of the table to inspect, e.g. 'my_table'
// const sourceTableId = 'my_source_table';

// The ID of the table where outputs are stored
// const outputTableId = 'my_output_table';

async function kAnonymityWithEntityIds() {
  // Specify the BigQuery table to analyze.
  const sourceTable = {
    projectId: projectId,
    datasetId: datasetId,
    tableId: sourceTableId,
  };

  // Specify the unique identifier in the source table for the k-anonymity analysis.
  const uniqueIdField = {name: 'Name'};

  // These values represent the column names of quasi-identifiers to analyze
  const quasiIds = [{name: 'Age'}, {name: 'Mystery'}];

  // Configure the privacy metric to compute for re-identification risk analysis.
  const privacyMetric = {
    kAnonymityConfig: {
      entityId: {
        field: uniqueIdField,
      },
      quasiIds: quasiIds,
    },
  };
  // Create action to publish job status notifications to BigQuery table.
  const action = [
    {
      saveFindings: {
        outputConfig: {
          table: {
            projectId: projectId,
            datasetId: datasetId,
            tableId: outputTableId,
          },
        },
      },
    },
  ];

  // Configure the risk analysis job to perform.
  const riskAnalysisJob = {
    sourceTable: sourceTable,
    privacyMetric: privacyMetric,
    actions: action,
  };
  // Combine configurations into a request for the service.
  const createDlpJobRequest = {
    parent: `projects/${projectId}/locations/global`,
    riskJob: riskAnalysisJob,
  };

  // Send the request and receive response from the service
  const [createdDlpJob] = await dlp.createDlpJob(createDlpJobRequest);
  const jobName = createdDlpJob.name;

  // Waiting for a maximum of 15 minutes for the job to get complete.
  let job;
  let numOfAttempts = 30;
  while (numOfAttempts > 0) {
    // Fetch DLP Job status
    [job] = await dlp.getDlpJob({name: jobName});

    // Check if the job has completed.
    if (job.state === 'DONE') {
      break;
    }
    if (job.state === 'FAILED') {
      console.log('Job Failed, Please check the configuration.');
      return;
    }
    // Sleep for a short duration before checking the job status again.
    await new Promise(resolve => {
      setTimeout(() => resolve(), 30000);
    });
    numOfAttempts -= 1;
  }

  // Create helper function for unpacking values
  const getValue = obj => obj[Object.keys(obj)[0]];

  // Print out the results.
  const histogramBuckets =
    job.riskDetails.kAnonymityResult.equivalenceClassHistogramBuckets;

  histogramBuckets.forEach((histogramBucket, histogramBucketIdx) => {
    console.log(`Bucket ${histogramBucketIdx}:`);
    console.log(
      `  Bucket size range: [${histogramBucket.equivalenceClassSizeLowerBound}, ${histogramBucket.equivalenceClassSizeUpperBound}]`
    );

    histogramBucket.bucketValues.forEach(valueBucket => {
      const quasiIdValues = valueBucket.quasiIdsValues
        .map(getValue)
        .join(', ');
      console.log(`  Quasi-ID values: {${quasiIdValues}}`);
      console.log(`  Class size: ${valueBucket.equivalenceClassSize}`);
    });
  });
}
await kAnonymityWithEntityIds();

PHP

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.

use Google\Cloud\Dlp\V2\DlpServiceClient;
use Google\Cloud\Dlp\V2\RiskAnalysisJobConfig;
use Google\Cloud\Dlp\V2\BigQueryTable;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\SaveFindings;
use Google\Cloud\Dlp\V2\EntityId;
use Google\Cloud\Dlp\V2\PrivacyMetric\KAnonymityConfig;
use Google\Cloud\Dlp\V2\PrivacyMetric;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\Dlp\V2\OutputStorageConfig;

/**
 * Computes the k-anonymity of a column set in a Google BigQuery table with entity id.
 *
 * @param string    $callingProjectId  The project ID to run the API call under.
 * @param string    $datasetId         The ID of the dataset to inspect.
 * @param string    $tableId           The ID of the table to inspect.
 * @param string[]  $quasiIdNames      Array columns that form a composite key (quasi-identifiers).
 */

function k_anonymity_with_entity_id(
    // TODO(developer): Replace sample parameters before running the code.
    string $callingProjectId,
    string $datasetId,
    string $tableId,
    array  $quasiIdNames
): void {
    // Instantiate a client.
    $dlp = new DlpServiceClient();

    // Specify the BigQuery table to analyze.
    $bigqueryTable = (new BigQueryTable())
        ->setProjectId($callingProjectId)
        ->setDatasetId($datasetId)
        ->setTableId($tableId);

    // Create a list of FieldId objects based on the provided list of column names.
    $quasiIds = array_map(
        function ($id) {
            return (new FieldId())
                ->setName($id);
        },
        $quasiIdNames
    );

    // Specify the unique identifier in the source table for the k-anonymity analysis.
    $statsConfig = (new KAnonymityConfig())
        ->setEntityId((new EntityId())
            ->setField((new FieldId())
                ->setName('Name')))
        ->setQuasiIds($quasiIds);

    // Configure the privacy metric to compute for re-identification risk analysis.
    $privacyMetric = (new PrivacyMetric())
        ->setKAnonymityConfig($statsConfig);

    // Specify the bigquery table to store the findings.
    // The "test_results" table in the given BigQuery dataset will be created if it doesn't
    // already exist.
    $outBigqueryTable = (new BigQueryTable())
        ->setProjectId($callingProjectId)
        ->setDatasetId($datasetId)
        ->setTableId('test_results');

    $outputStorageConfig = (new OutputStorageConfig())
        ->setTable($outBigqueryTable);

    $findings = (new SaveFindings())
        ->setOutputConfig($outputStorageConfig);

    $action = (new Action())
        ->setSaveFindings($findings);

    // Construct risk analysis job config to run.
    $riskJob = (new RiskAnalysisJobConfig())
        ->setPrivacyMetric($privacyMetric)
        ->setSourceTable($bigqueryTable)
        ->setActions([$action]);

    // Submit request.
    $parent = "projects/$callingProjectId/locations/global";
    $job = $dlp->createDlpJob($parent, [
        'riskJob' => $riskJob
    ]);

    $numOfAttempts = 10;
    do {
        printf('Waiting for job to complete' . PHP_EOL);
        sleep(10);
        $job = $dlp->getDlpJob($job->getName());
        if ($job->getState() == JobState::DONE) {
            break;
        }
        $numOfAttempts--;
    } while ($numOfAttempts > 0);

    // Print finding counts
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $histBuckets = $job->getRiskDetails()->getKAnonymityResult()->getEquivalenceClassHistogramBuckets();

            foreach ($histBuckets as $bucketIndex => $histBucket) {
                // Print bucket stats.
                printf('Bucket %s:' . PHP_EOL, $bucketIndex);
                printf(
                    '  Bucket size range: [%s, %s]' . PHP_EOL,
                    $histBucket->getEquivalenceClassSizeLowerBound(),
                    $histBucket->getEquivalenceClassSizeUpperBound()
                );

                // Print bucket values.
                foreach ($histBucket->getBucketValues() as $percent => $valueBucket) {
                    // Pretty-print quasi-ID values.
                    printf('  Quasi-ID values:' . PHP_EOL);
                    foreach ($valueBucket->getQuasiIdsValues() as $index => $value) {
                        print('    ' . $value->serializeToJsonString() . PHP_EOL);
                    }
                    printf(
                        '  Class size: %s' . PHP_EOL,
                        $valueBucket->getEquivalenceClassSize()
                    );
                }
            }

            break;
        case JobState::FAILED:
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            $errors = $job->getErrors();
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            printf('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            printf('Unexpected job state. Most likely, the job is either running or has not yet started.');
    }
}

Python

Per scoprire come installare e utilizzare la libreria client per Sensitive Data Protection, consulta Librerie client di Sensitive Data Protection.

import time
from typing import List

import google.cloud.dlp_v2
from google.cloud.dlp_v2 import types


def k_anonymity_with_entity_id(
    project: str,
    source_table_project_id: str,
    source_dataset_id: str,
    source_table_id: str,
    entity_id: str,
    quasi_ids: List[str],
    output_table_project_id: str,
    output_dataset_id: str,
    output_table_id: str,
) -> None:
    """Uses the Data Loss Prevention API to compute the k-anonymity using entity_id
        of a column set in a Google BigQuery table.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        source_table_project_id: The Google Cloud project id where the BigQuery table
            is stored.
        source_dataset_id: The id of the dataset to inspect.
        source_table_id: The id of the table to inspect.
        entity_id: The column name of the table that enables accurately determining k-anonymity
         in the common scenario wherein several rows of dataset correspond to the same sensitive
         information.
        quasi_ids: A set of columns that form a composite key.
        output_table_project_id: The Google Cloud project id where the output BigQuery table
            is stored.
        output_dataset_id: The id of the output BigQuery dataset.
        output_table_id: The id of the output BigQuery table.
    """

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Location info of the source BigQuery table.
    source_table = {
        "project_id": source_table_project_id,
        "dataset_id": source_dataset_id,
        "table_id": source_table_id,
    }

    # Specify the bigquery table to store the findings.
    # The output_table_id in the given BigQuery dataset will be created if it doesn't
    # already exist.
    dest_table = {
        "project_id": output_table_project_id,
        "dataset_id": output_dataset_id,
        "table_id": output_table_id,
    }

    # Convert quasi id list to Protobuf type
    def map_fields(field: str) -> dict:
        return {"name": field}

    #  Configure column names of quasi-identifiers to analyze
    quasi_ids = map(map_fields, quasi_ids)

    # Tell the API where to send a notification when the job is complete.
    actions = [{"save_findings": {"output_config": {"table": dest_table}}}]

    # Configure the privacy metric to compute for re-identification risk analysis.
    # Specify the unique identifier in the source table for the k-anonymity analysis.
    privacy_metric = {
        "k_anonymity_config": {
            "entity_id": {"field": {"name": entity_id}},
            "quasi_ids": quasi_ids,
        }
    }

    # Configure risk analysis job.
    risk_job = {
        "privacy_metric": privacy_metric,
        "source_table": source_table,
        "actions": actions,
    }

    # Convert the project id into a full resource id.
    parent = f"projects/{project}/locations/global"

    # Call API to start risk analysis job.
    response = dlp.create_dlp_job(
        request={
            "parent": parent,
            "risk_job": risk_job,
        }
    )
    job_name = response.name
    print(f"Inspection Job started : {job_name}")

    # Waiting for a maximum of 15 minutes for the job to be completed.
    job = dlp.get_dlp_job(request={"name": job_name})
    no_of_attempts = 30
    while no_of_attempts > 0:
        # Check if the job has completed
        if job.state == google.cloud.dlp_v2.DlpJob.JobState.DONE:
            break
        if job.state == google.cloud.dlp_v2.DlpJob.JobState.FAILED:
            print("Job Failed, Please check the configuration.")
            return

        # Sleep for a short duration before checking the job status again
        time.sleep(30)
        no_of_attempts -= 1

        # Get the DLP job status
        job = dlp.get_dlp_job(request={"name": job_name})

    if job.state != google.cloud.dlp_v2.DlpJob.JobState.DONE:
        print("Job did not complete within 15 minutes.")
        return

    # Create helper function for unpacking values
    def get_values(obj: types.Value) -> str:
        return str(obj.string_value)

    # Print out the results.
    print(f"Job name: {job.name}")
    histogram_buckets = (
        job.risk_details.k_anonymity_result.equivalence_class_histogram_buckets
    )
    # Print bucket stats
    for i, bucket in enumerate(histogram_buckets):
        print(f"Bucket {i}:")
        if bucket.equivalence_class_size_lower_bound:
            print(
                f"Bucket size range: [{bucket.equivalence_class_size_lower_bound}, "
                f"{bucket.equivalence_class_size_upper_bound}]"
            )
            for value_bucket in bucket.bucket_values:
                print(
                    f"Quasi-ID values: {get_values(value_bucket.quasi_ids_values[0])}"
                )
                print(f"Class size: {value_bucket.equivalence_class_size}")
        else:
            print("No findings.")

Passaggi successivi

Scopri come calcolare il valore l-diversity per un set di dati.
Scopri come calcolare il valore della k-map per un set di dati.
Scopri come calcolare il valore δ-presence per un set di dati.