Vous pouvez estimer les valeurs de k-table à l'aide de Cloud DLP, qui utilise un modèle statistique permettant d'évaluer un ensemble de données de restauration de l'identification.
En savoir plus
Pour obtenir une documentation détaillée incluant cet exemple de code, consultez les articles suivants :
Exemple de code
C#
Pour savoir comment installer et utiliser la bibliothèque cliente pour la protection des données sensibles, consultez Bibliothèques clientes pour 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;
using static Google.Cloud.Dlp.V2.PrivacyMetric.Types.KMapEstimationConfig.Types;
public class RiskAnalysisCreateKMap
{
public static object KMap(
string callingProjectId,
string tableProjectId,
string datasetId,
string tableId,
string topicId,
string subscriptionId,
IEnumerable<FieldId> quasiIds,
IEnumerable<InfoType> infoTypes,
string regionCode)
{
var dlp = DlpServiceClient.Create();
// Construct + submit the job
var kmapEstimationConfig = new KMapEstimationConfig
{
QuasiIds =
{
quasiIds.Zip(
infoTypes,
(Field, InfoType) => new TaggedField
{
Field = Field,
InfoType = InfoType
}
)
},
RegionCode = regionCode
};
var config = new RiskAnalysisJobConfig()
{
PrivacyMetric = new PrivacyMetric
{
KMapEstimationConfig = kmapEstimationConfig
},
SourceTable = new BigQueryTable
{
ProjectId = tableProjectId,
DatasetId = datasetId,
TableId = tableId
},
Actions =
{
new Google.Cloud.Dlp.V2.Action
{
PubSub = new PublishToPubSub
{
Topic = $"projects/{callingProjectId}/topics/{topicId}"
}
}
}
};
var submittedJob = dlp.CreateDlpJob(
new CreateDlpJobRequest
{
ParentAsProjectName = new ProjectName(callingProjectId),
RiskJob = config
});
// Listen to pub/sub for the job
var subscriptionName = new SubscriptionName(
callingProjectId,
subscriptionId);
var subscriber = SubscriberClient.CreateAsync(
subscriptionName).Result;
// SimpleSubscriber runs your message handle function on multiple
// threads to maximize throughput.
var done = new ManualResetEventSlim(false);
subscriber.StartAsync((PubsubMessage message, CancellationToken cancel) =>
{
if (message.Attributes["DlpJobName"] == submittedJob.Name)
{
Thread.Sleep(500); // Wait for DLP API results to become consistent
done.Set();
return Task.FromResult(SubscriberClient.Reply.Ack);
}
else
{
return Task.FromResult(SubscriberClient.Reply.Nack);
}
});
done.Wait(TimeSpan.FromMinutes(10)); // 10 minute timeout; may not work for large jobs
subscriber.StopAsync(CancellationToken.None).Wait();
// Process results
var resultJob = dlp.GetDlpJob(new GetDlpJobRequest
{
DlpJobName = DlpJobName.Parse(submittedJob.Name)
});
var result = resultJob.RiskDetails.KMapEstimationResult;
for (var histogramIdx = 0; histogramIdx < result.KMapEstimationHistogram.Count; histogramIdx++)
{
var histogramValue = result.KMapEstimationHistogram[histogramIdx];
Console.WriteLine($"Bucket {histogramIdx}");
Console.WriteLine($" Anonymity range: [{histogramValue.MinAnonymity}, {histogramValue.MaxAnonymity}].");
Console.WriteLine($" Size: {histogramValue.BucketSize}");
foreach (var datapoint in histogramValue.BucketValues)
{
// 'UnpackValue(x)' is a prettier version of 'x.toString()'
Console.WriteLine($" Values: [{String.Join(',', datapoint.QuasiIdsValues.Select(x => UnpackValue(x)))}]");
Console.WriteLine($" Estimated k-map anonymity: {datapoint.EstimatedAnonymity}");
}
}
return 0;
}
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 Bibliothèques clientes pour 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.
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"
"github.com/golang/protobuf/ptypes/empty"
)
// riskKMap runs K Map on the given data.
func riskKMap(w io.Writer, projectID, dataProject, pubSubTopic, pubSubSub, datasetID, tableID, region string, columnNames ...string) error {
// projectID := "my-project-id"
// dataProject := "bigquery-public-data"
// pubSubTopic := "dlp-risk-sample-topic"
// pubSubSub := "dlp-risk-sample-sub"
// datasetID := "san_francisco"
// tableID := "bikeshare_trips"
// region := "US"
// columnNames := "zip_code"
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.PrivacyMetric_KMapEstimationConfig_TaggedField
for _, c := range columnNames {
q = append(q, &dlppb.PrivacyMetric_KMapEstimationConfig_TaggedField{
Field: &dlppb.FieldId{
Name: c,
},
Tag: &dlppb.PrivacyMetric_KMapEstimationConfig_TaggedField_Inferred{
Inferred: &empty.Empty{},
},
})
}
// 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_KMapEstimationConfig_{
KMapEstimationConfig: &dlppb.PrivacyMetric_KMapEstimationConfig{
QuasiIds: q,
RegionCode: region,
},
},
},
// 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().GetKMapEstimationResult().GetKMapEstimationHistogram()
for i, b := range h {
fmt.Fprintf(w, "Histogram bucket %v\n", i)
fmt.Fprintf(w, " Anonymity range: [%v,%v]\n", b.GetMaxAnonymity(), b.GetMaxAnonymity())
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, " Estimated anonymity: %v\n", v.GetEstimatedAnonymity())
}
}
// Stop listening for more messages.
cancel()
})
if err != nil {
return fmt.Errorf("Recieve: %w", err)
}
return nil
}
Java
Pour savoir comment installer et utiliser la bibliothèque cliente pour la protection des données sensibles, consultez Bibliothèques clientes pour 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.
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.KMapEstimationResult;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KMapEstimationResult.KMapEstimationHistogramBucket;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KMapEstimationResult.KMapEstimationQuasiIdValues;
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.InfoType;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.PrivacyMetric;
import com.google.privacy.dlp.v2.PrivacyMetric.KMapEstimationConfig;
import com.google.privacy.dlp.v2.PrivacyMetric.KMapEstimationConfig.TaggedField;
import com.google.privacy.dlp.v2.RiskAnalysisJobConfig;
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.ArrayList;
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 RiskAnalysisKMap {
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";
calculateKMap(projectId, datasetId, tableId, topicId, subscriptionId);
}
public static void calculateKMap(
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", "Gender");
// These values represent the info types corresponding to the quasi-identifiers above
List<String> infoTypeNames = Arrays.asList("AGE", "GENDER");
// Tag each of the quasiId column names with its corresponding infoType
List<InfoType> infoTypes =
infoTypeNames.stream()
.map(it -> InfoType.newBuilder().setName(it).build())
.collect(Collectors.toList());
if (quasiIds.size() != infoTypes.size()) {
throw new IllegalArgumentException("The numbers of quasi-IDs and infoTypes must be equal!");
}
List<TaggedField> taggedFields = new ArrayList<TaggedField>();
for (int i = 0; i < quasiIds.size(); i++) {
TaggedField taggedField =
TaggedField.newBuilder()
.setField(FieldId.newBuilder().setName(quasiIds.get(i)).build())
.setInfoType(infoTypes.get(i))
.build();
taggedFields.add(taggedField);
}
// The k-map distribution region can be specified by any ISO-3166-1 region code.
String regionCode = "US";
// Configure the privacy metric for the job
KMapEstimationConfig kmapConfig =
KMapEstimationConfig.newBuilder()
.addAllQuasiIds(taggedFields)
.setRegionCode(regionCode)
.build();
PrivacyMetric privacyMetric =
PrivacyMetric.newBuilder().setKMapEstimationConfig(kmapConfig).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
KMapEstimationResult kmapResult = completedJob.getRiskDetails().getKMapEstimationResult();
for (KMapEstimationHistogramBucket result : kmapResult.getKMapEstimationHistogramList()) {
System.out.printf(
"\tAnonymity range: [%d, %d]\n", result.getMinAnonymity(), result.getMaxAnonymity());
System.out.printf("\tSize: %d\n", result.getBucketSize());
for (KMapEstimationQuasiIdValues valueBucket : result.getBucketValuesList()) {
List<String> quasiIdValues =
valueBucket.getQuasiIdsValuesList().stream()
.map(
value -> {
String s = value.toString();
return s.substring(s.indexOf(':') + 1).trim();
})
.collect(Collectors.toList());
System.out.printf("\tValues: {%s}\n", String.join(", ", quasiIdValues));
System.out.printf(
"\tEstimated k-map anonymity: %d\n", valueBucket.getEstimatedAnonymity());
}
}
}
}
// 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 Bibliothèques clientes pour 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.
// 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'
// The ISO 3166-1 region code that the data is representative of
// Can be omitted if using a region-specific infoType (such as US_ZIP_5)
// const regionCode = 'USA';
// A set of columns that form a composite key ('quasi-identifiers'), and
// optionally their reidentification distributions
// const quasiIds = [{ field: { name: 'age' }, infoType: { name: 'AGE' }}];
async function kMapEstimationAnalysis() {
const sourceTable = {
projectId: tableProjectId,
datasetId: datasetId,
tableId: tableId,
};
// Construct request for creating a risk analysis job
const request = {
parent: `projects/${projectId}/locations/global`,
riskJob: {
privacyMetric: {
kMapEstimationConfig: {
quasiIds: quasiIds,
regionCode: regionCode,
},
},
sourceTable: sourceTable,
actions: [
{
pubSub: {
topic: `projects/${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.kMapEstimationResult.kMapEstimationHistogram;
histogramBuckets.forEach((histogramBucket, histogramBucketIdx) => {
console.log(`Bucket ${histogramBucketIdx}:`);
console.log(
` Anonymity range: [${histogramBucket.minAnonymity}, ${histogramBucket.maxAnonymity}]`
);
console.log(` Size: ${histogramBucket.bucketSize}`);
histogramBucket.bucketValues.forEach(valueBucket => {
const values = valueBucket.quasiIdsValues.map(value => getValue(value));
console.log(` Values: ${values.join(' ')}`);
console.log(
` Estimated k-map anonymity: ${valueBucket.estimatedAnonymity}`
);
});
});
}
await kMapEstimationAnalysis();PHP
Pour savoir comment installer et utiliser la bibliothèque cliente pour la protection des données sensibles, consultez Bibliothèques clientes pour 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.
use Exception;
use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
use Google\Cloud\Dlp\V2\BigQueryTable;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\InfoType;
use Google\Cloud\Dlp\V2\PrivacyMetric;
use Google\Cloud\Dlp\V2\PrivacyMetric\KMapEstimationConfig;
use Google\Cloud\Dlp\V2\PrivacyMetric\KMapEstimationConfig\TaggedField;
use Google\Cloud\Dlp\V2\RiskAnalysisJobConfig;
use Google\Cloud\PubSub\PubSubClient;
/**
* Computes the k-map risk estimation 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 $regionCode The ISO 3166-1 region code that the data is representative of
* @param string[] $quasiIdNames Array columns that form a composite key (quasi-identifiers)
* @param string[] $infoTypes Array of infoTypes corresponding to the chosen quasi-identifiers
*/
function k_map(
string $callingProjectId,
string $dataProjectId,
string $topicId,
string $subscriptionId,
string $datasetId,
string $tableId,
string $regionCode,
array $quasiIdNames,
array $infoTypes
): void {
// Instantiate a client.
$dlp = new DlpServiceClient();
$pubsub = new PubSubClient();
$topic = $pubsub->topic($topicId);
// Verify input
if (count($infoTypes) != count($quasiIdNames)) {
throw new Exception('Number of infoTypes and number of quasi-identifiers must be equal!');
}
// Map infoTypes to quasi-ids
$quasiIdObjects = array_map(function ($quasiId, $infoType) {
$quasiIdField = (new FieldId())
->setName($quasiId);
$quasiIdType = (new InfoType())
->setName($infoType);
$quasiIdObject = (new TaggedField())
->setInfoType($quasiIdType)
->setField($quasiIdField);
return $quasiIdObject;
}, $quasiIdNames, $infoTypes);
// Construct analysis config
$statsConfig = (new KMapEstimationConfig())
->setQuasiIds($quasiIdObjects)
->setRegionCode($regionCode);
$privacyMetric = (new PrivacyMetric())
->setKMapEstimationConfig($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 = $job->getRiskDetails()->getKMapEstimationResult()->getKMapEstimationHistogram();
foreach ($histBuckets as $bucketIndex => $histBucket) {
// Print bucket stats
printf('Bucket %s:' . PHP_EOL, $bucketIndex);
printf(
' Anonymity range: [%s, %s]' . PHP_EOL,
$histBucket->getMinAnonymity(),
$histBucket->getMaxAnonymity()
);
printf(' Size: %s' . PHP_EOL, $histBucket->getBucketSize());
// Print bucket values
foreach ($histBucket->getBucketValues() as $percent => $valueBucket) {
printf(
' Estimated k-map anonymity: %s' . PHP_EOL,
$valueBucket->getEstimatedAnonymity()
);
// Pretty-print quasi-ID values
print(' Values: ' . PHP_EOL);
foreach ($valueBucket->getQuasiIdsValues() as $index => $value) {
print(' ' . $value->serializeToJsonString() . PHP_EOL);
}
}
}
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 Bibliothèques clientes pour 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.
import concurrent.futures
from typing import List
import google.cloud.dlp
from google.cloud.dlp_v2 import types
import google.cloud.pubsub
def k_map_estimate_analysis(
project: str,
table_project_id: str,
dataset_id: str,
table_id: str,
topic_id: str,
subscription_id: str,
quasi_ids: List[str],
info_types: List[str],
region_code: str = "US",
timeout: int = 300,
) -> None:
"""Uses the Data Loss Prevention API to compute the k-map risk estimation
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 and optionally
their re-identification distributions.
info_types: Type of information of the quasi_id in order to provide a
statistical model of population.
region_code: The ISO 3166-1 region code that the data is representative
of. Can be omitted if using a region-specific infoType (such as
US_ZIP_5)
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 full resource ids.
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,
}
# Check that numbers of quasi-ids and info types are equal
if len(quasi_ids) != len(info_types):
raise ValueError(
"""Number of infoTypes and number of quasi-identifiers
must be equal!"""
)
# Convert quasi id list to Protobuf type
def map_fields(quasi_id: str, info_type: str) -> dict:
return {"field": {"name": quasi_id}, "info_type": {"name": info_type}}
quasi_ids = map(map_fields, quasi_ids, info_types)
# 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_map_estimation_config": {
"quasi_ids": quasi_ids,
"region_code": region_code,
}
},
"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_map_estimation_result.k_map_estimation_histogram
)
# Print bucket stats
for i, bucket in enumerate(histogram_buckets):
print(f"Bucket {i}:")
print(
" Anonymity range: [{}, {}]".format(
bucket.min_anonymity, bucket.max_anonymity
)
)
print(f" Size: {bucket.bucket_size}")
for value_bucket in bucket.bucket_values:
print(
" Values: {}".format(
map(get_values, value_bucket.quasi_ids_values)
)
)
print(
" Estimated k-map anonymity: {}".format(
value_bucket.estimated_anonymity
)
)
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()
Étapes suivantes
Pour rechercher et filtrer des exemples de code pour d'autres produits Google Cloud, consultez l'exemple de navigateur Google Cloud.