Cloud Data Loss Prevention (Cloud DLP) fait désormais partie de la protection des données sensibles. Le nom de l'API reste le même: API Cloud Data Loss Prevention (API DLP). Pour en savoir plus sur les services qui constituent la protection des données sensibles, consultez la présentation de la protection des données sensibles.

Cette page a été traduite par l'API Cloud Translation.

Calculer le k-anonymat

K-anonymat est une propriété d'ensemble de données qui indique la possibilité de restaurer l'identification de ses enregistrements. Un ensemble de données est considéré comme k-anonyme si les quasi-identifiants de chaque individu dans l'ensemble de données sont identiques à au moins k - 1 autres individus figurant également dans l'ensemble de données. Cet exemple montre comment utiliser Cloud DLP pour calculer une valeur k-anonymat.

En savoir plus

Pour obtenir une documentation détaillée incluant cet exemple de code, consultez les articles suivants :

Calculer k-anonymat pour un ensemble de données

Exemple de code

C#

Pour savoir comment installer et utiliser la bibliothèque cliente pour la protection des données sensibles, consultez la page Bibliothèques clientes de la protection des données sensibles.

Pour vous authentifier auprès de la protection des données sensibles, configurez les Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.


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

Pour savoir comment installer et utiliser la bibliothèque cliente pour la protection des données sensibles, consultez la page Bibliothèques clientes de la protection des données sensibles.

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

Pour savoir comment installer et utiliser la bibliothèque cliente pour la protection des données sensibles, consultez la page Bibliothèques clientes de la protection des données sensibles.


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

Pour savoir comment installer et utiliser la bibliothèque cliente pour la protection des données sensibles, consultez la page Bibliothèques clientes de la protection des données sensibles.

// 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

Pour savoir comment installer et utiliser la bibliothèque cliente pour la protection des données sensibles, consultez la page Bibliothèques clientes de la protection des données sensibles.

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

Pour savoir comment installer et utiliser la bibliothèque cliente pour la protection des données sensibles, consultez la page Bibliothèques clientes de la protection des données sensibles.


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()

Étape suivante

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