Computing k-anonymity for a dataset

K-anonymity is a property of a dataset that indicates the re-identifiability of its records. A dataset is k-anonymous if quasi-identifiers for each person in the dataset are identical to at least k – 1 other people also in the dataset.

You can compute the k-anonymity value based on one or more columns, or fields, of a dataset. This topic demonstrates how to compute k-anonymity values for a dataset using Cloud Data Loss Prevention (DLP). For more information about k-anonymity or risk analysis in general, see the risk analysis concept topic before continuing on.

Before you begin

Before continuing, be sure you've done the following:

  1. Sign in to your Google Account.
  2. In the Google Cloud Console, on the project selector page, select or create a Google Cloud project.
  3. Go to the project selector
  4. Make sure that billing is enabled for your Google Cloud project. Learn how to confirm billing is enabled for your project.
  5. Enable Cloud DLP.
  6. Enable Cloud DLP

  7. Select a BigQuery dataset to analyze. Cloud DLP calculates the k-anonymity metric by scanning a BigQuery table.
  8. Determine an identifier (if applicable) and at least one quasi-identifier in the dataset. For more information, see Risk analysis terms and techniques.

Compute k-anonymity

Cloud DLP performs risk analysis whenever a risk analysis job runs. You must create the job first, either by using the Cloud Console, sending a DLP API request, or using a Cloud DLP client library.


  1. In the Cloud Console, open Cloud DLP.

    Go to Cloud DLP

  2. On the Create menu, point to Job or job trigger, and then select Re-identification risk analysis.

  3. In the Choose input data section of the New risk analysis job page, first specify the BigQuery table to scan by entering the project ID of the project containing the table, the dataset ID of the table, and the name of the table where specified.

  4. Under Privacy metric to compute, select k-anonymity.

  5. In the Job ID section, you can optionally give the job a custom identifier and select a resource location in which Cloud DLP will process your data. When you're done, click Continue.

  6. In the Define fields section, you specify identifiers and quasi-identifiers for the k-anonymity risk job. Cloud DLP accesses the metadata of the BigQuery table you specified in the previous step and attempts to populate the list of fields.

    1. Select the appropriate checkbox to specify a field as either an identifier (ID) or quasi-identifier (QI). You must select either 0 or 1 identifiers and at least 1 quasi-identifier.
    2. If Cloud DLP isn't able to populate the fields, click Enter field name to manually enter one or more fields and set each one as identifier or quasi-identifier. When you're done, click Continue.
  7. In the Add actions section, you can add optional actions to perform when the risk job is complete. The available options are:

    • Save to BigQuery: Saves the results of the risk analysis scan to a BigQuery table.
    • Publish to Pub/Sub: Publishes a notification to a Pub/Sub topic.
    • Notify by email: Sends you an email with results. When you're done, click Create.

The k-anonymity risk analysis job starts immediately.


To run a new risk analysis job to compute k-anonymity, send a request to the projects.dlpJobs resource, where PROJECT_ID indicates your project identifier:

The request contains a RiskAnalysisJobConfig object, which is composed of the following:

  • A PrivacyMetric object. This is where you specify that you're calculating k-anonymity by including a KAnonymityConfig object.

  • A BigQueryTable object. Specify the BigQuery table to scan by including all of the following:

    • projectId: The project ID of the project containing the table.
    • datasetId: The dataset ID of the table.
    • tableId: The name of the table.
  • A set of one or more Action objects, which represent actions to run, in the order given, at the completion of the job. Each Action object can contain one of the following actions:

    Within the KAnonymityConfig object, you specify the following:

    • quasiIds[]: One or more quasi-identifiers (FieldId objects) to scan and use to compute k-anonymity. When you specify multiple quasi-identifiers, they are considered a single composite key. Structs and repeated data types are not supported, but nested fields are supported as long as they are not structs themselves or nested within a repeated field.
    • entityId: Optional identifier value that, when set, indicates that all rows corresponding to each distinct entityId should be grouped together for k-anonymity computation. Typically, an entityId will be a column that represents a unique user, like a customer ID or a user ID. When an entityId appears on several rows with different quasi-identifier values, these rows will be joined to form a multiset that will be used as the quasi-identifiers for that entity. For more information about entity IDs, see Entity IDs and computing k-anonymity in the Risk analysis conceptual topic.

As soon as you send a request to the DLP API, it starts the risk analysis job.


To learn how to install and use the client library for Cloud DLP, see Cloud DLP client libraries.

import java.util.Arrays;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

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 =

      // 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 =

              .map(columnName -> FieldId.newBuilder().setName(columnName).build())
      KAnonymityConfig kanonymityConfig =
      PrivacyMetric privacyMetric =

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

      // Configure the risk analysis job to perform
      RiskAnalysisJobConfig riskAnalysisJobConfig =

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

      // 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();

      // 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.");
      } finally {

      // Build a request to get the completed job
      GetDlpJobRequest getDlpJobRequest =

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

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

        for (KAnonymityEquivalenceClass bucket : result.getBucketValuesList()) {
          List<String> quasiIdValues =

          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)) {
    } else {


To learn how to install and use the client library for Cloud DLP, see Cloud DLP client libraries.

// 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 =;
  // 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) {
        subscription.removeListener('message', messageHandler);
        subscription.removeListener('error', errorHandler);
      } else {

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

    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 =

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

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


To learn how to install and use the client library for Cloud DLP, see Cloud DLP client libraries.

def k_anonymity_analysis(
    """Uses the Data Loss Prevention API to compute the k-anonymity of a
        column set in a Google BigQuery table.
        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
        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.

        None; the response from the API is printed to the terminal.
    import concurrent.futures

    # Import the client library.

    # This sample additionally uses Cloud Pub/Sub to receive results from
    # potentially long-running operations.

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

    # Instantiate a client.
    dlp =

    # Convert the project id into a full resource id.
    topic =, 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):
        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):
        if message.attributes["DlpJobName"] ==
            # This is the message we're looking for, so acknowledge it.

            # Now that the job is done, fetch the results and print them.
            job = dlp.get_dlp_job(request={"name":})
            histogram_buckets = (
            # Print bucket stats
            for i, bucket in enumerate(histogram_buckets):
                print("Bucket {}:".format(i))
                if bucket.equivalence_class_size_lower_bound:
                        "   Bucket size range: [{}, {}]".format(
                    for value_bucket in bucket.bucket_values:
                            "   Quasi-ID values: {}".format(
                                map(get_values, value_bucket.quasi_ids_values)
                            "   Class size: {}".format(
            # This is not the message we're looking for.

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

    except concurrent.futures.TimeoutError:
            "No event received before the timeout. Please verify that the "
            "subscription provided is subscribed to the topic provided."


To learn how to install and use the client library for Cloud DLP, see Cloud DLP client libraries.

import (

	dlp ""
	dlppb ""

// 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: %v", err)

	// Create a PubSub Client used to listen for when the inspect job finishes.
	pubsubClient, err := pubsub.NewClient(ctx, projectID)
	if err != nil {
		return fmt.Errorf("Error creating PubSub client: %v", err)
	defer pubsubClient.Close()

	// Create a PubSub subscription we can use to listen for messages.
	s, err := setupPubSub(projectID, pubSubTopic, pubSubSub)
	if err != nil {
		return fmt.Errorf("setupPubSub: %v", 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: %v", 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() {
		time.Sleep(500 * time.Millisecond)
		j, err := client.GetDlpJob(ctx, &dlppb.GetDlpJobRequest{
			Name: j.GetName(),
		if err != nil {
			fmt.Fprintf(w, "GetDlpJob: %v", err)
		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.
	if err != nil {
		return fmt.Errorf("Receive: %v", err)
	return nil


To learn how to install and use the client library for Cloud DLP, see Cloud DLP client libraries.

 * Computes the k-anonymity of a column set in a Google BigQuery table.
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\PublishToPubSub;
use Google\Cloud\Dlp\V2\PrivacyMetric\KAnonymityConfig;
use Google\Cloud\Dlp\V2\PrivacyMetric;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\PubSub\PubSubClient;

/** Uncomment and populate these variables in your code */
// $callingProjectId = 'The project ID to run the API call under';
// $dataProjectId = 'The project ID containing the target Datastore';
// $topicId = 'The name of the Pub/Sub topic to notify once the job completes';
// $subscriptionId = 'The name of the Pub/Sub subscription to use when listening for job';
// $datasetId = 'The ID of the dataset to inspect';
// $tableId = 'The ID of the table to inspect';
// $quasiIdNames = 'Comma-separated list of columns that form a composite key (quasi-identifiers)';

// Instantiate a client.
$dlp = new DlpServiceClient([
    'projectId' => $callingProjectId,
$pubsub = new PubSubClient([
    'projectId' => $callingProjectId,
$topic = $pubsub->topic($topicId);

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

$statsConfig = (new KAnonymityConfig())

$privacyMetric = (new PrivacyMetric())

// Construct items to be analyzed
$bigqueryTable = (new BigQueryTable())

// Construct the action to run when job completes
$pubSubAction = (new PublishToPubSub())

$action = (new Action())

// Construct risk analysis job config to run
$riskJob = (new RiskAnalysisJobConfig())

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

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

// 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()) {
            // Get the updated job. Loop to avoid race condition with DLP API.
            do {
                $job = $dlp->getDlpJob($job->getName());
            } while ($job->getState() == JobState::RUNNING);
            break 2; // break from parent do while
    printf('Waiting for job to complete' . PHP_EOL);
    // Exponential backoff with max delay of 60 seconds
    sleep(min(60, pow(2, ++$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);
                '  Bucket size range: [%s, %s]' . PHP_EOL,

            // 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);
                    '  Class size: %s' . PHP_EOL,

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


To learn how to install and use the client library for Cloud DLP, see Cloud DLP client libraries.

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(

        // 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
                return Task.FromResult(SubscriberClient.Reply.Ack);
                return Task.FromResult(SubscriberClient.Reply.Nack);

        done.Wait(TimeSpan.FromMinutes(10)); // 10 minute timeout; may not work for large jobs

        // 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();

List completed risk analysis jobs

You can view a list of the risk analysis jobs that have been run in the current project.


To list running and previously run risk analysis jobs in the Cloud Console, do the following:

  1. In the Cloud Console, open Cloud DLP.

    Go to Cloud DLP

  2. Click the Jobs & job triggers tab at the top of the page.

  3. Click the Risk jobs tab.

The risk job listing appears.


To list running and previously run risk analysis jobs, send a GET request to the projects.dlpJobs resource. Adding a job type filter (?type=RISK_ANALYSIS_JOB) narrows the response to only risk analysis jobs.

The response you receive contains a JSON representation of all current and previous risk analysis jobs.

View k-anonymity job results

Cloud DLP in the Cloud Console features built-in visualizations for completed k-anonymity jobs. After following the instructions in the previous section, from the risk analysis job listing, select the job for which you want to view results. Assuming the job has run successfully, the top of the Risk analysis details page looks like this:

At the top of the page is information about the k-anonymity risk job, including its job ID and, under Container, its resource location.

To view the results of the k-anonymity calculation, click the K-anonymity tab. To view the risk analysis job's configuration, click the Configuration tab.

The K-anonymity tab first lists the entity ID (if any) and the quasi-identifiers used to calculate k-anonymity.

Risk chart

The Re-identification risk chart plots, on the y-axis, the potential percentage of data loss for both unique rows and unique quasi-identifier combinations to achieve, on the x-axis, a k-anonymity value. The chart's color also indicates risk potential. Darker shades of blue indicate a higher risk, while lighter shades indicate less risk.

Higher k-anonymity values indicate less risk of re-identification. To achieve higher k-anonymity values, however, you would need to remove higher percentages of the total rows and higher unique quasi-identifier combinations, which might decrease the utility of the data. To see a specific potential percentage loss value for a certain k-anonymity value, hover your cursor over the chart. As shown in the screenshot, a tooltip appears on the chart.

To view more detail about a specific k-anonymity value, click the corresponding data point. A detailed explanation is shown under the chart and a sample data table appears further down the page.

Risk sample data table

The second component to the risk job results page is the sample data table. It displays quasi-identifier combinations for a given target k-anonymity value.

The first column of the table lists the k-anonymity values. Click a k-anonymity value to view corresponding sample data that would need to be dropped to achieve that value.

The second column displays the respective potential data loss of unique rows and quasi-identifier combinations, as well as the number of groups with at least k records and the total number of records.

The last column displays a sample of groups that share a quasi-identifier combination, along with the number of records that exist for that combination.

Retrieve job details using REST

To retrieve the results of the k-anonymity risk analysis job using the REST API, send the following GET request to the projects.dlpJobs resource. Replace PROJECT_ID with your project ID and JOB_ID with the identifier of the job you want to obtain results for. The job ID was returned when you started the job, and can also be retrieved by listing all jobs.


The request returns a JSON object containing an instance of the job. The results of the analysis are inside the "riskDetails" key, in an AnalyzeDataSourceRiskDetails object. For more information, see the API reference for the DlpJob resource.

What's next

  • Learn how to calculate the l-diversity value for a dataset.
  • Learn how to calculate the k-map value for a dataset.
  • Learn how to calculate the δ-presence value for a dataset.