Cloud Data Loss Prevention (Cloud DLP) kini menjadi bagian dari Sensitive Data Protection. Nama API tetap sama: Cloud Data Loss Prevention API (DLP API). Untuk informasi tentang layanan yang membentuk Perlindungan Data Sensitif, lihat Ringkasan Perlindungan Data Sensitif.
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Kehadiran delta (δ-presence) adalah metrik yang mengukur probabilitas bahwa seseorang termasuk dalam set data yang dianalisis. Seperti peta k,
Anda dapat memperkirakan nilai kehadiran δ menggunakan Perlindungan Data Sensitif, yang
menggunakan model statistik untuk memperkirakan set data serangan.
Kehadiran δ berbeda dengan metode analisis risiko lainnya, yang
dataset serangannya diketahui secara eksplisit. Bergantung pada jenis data, Perlindungan Data Sensitif menggunakan set data yang tersedia secara publik (misalnya, dari Sensus AS) atau model statistik kustom (misalnya, satu atau beberapa tabel BigQuery yang Anda tentukan), atau mengekstrapolasi dari distribusi nilai dalam set data input Anda.
Topik ini menunjukkan cara menghitung nilai kehadiran δ untuk set data menggunakan
Sensitive Data Protection. Untuk mengetahui informasi selengkapnya tentang analisis risiko atau kehadiran δ secara umum, lihat topik konsep analisis risiko sebelum melanjutkan.
Sebelum memulai
Sebelum melanjutkan, pastikan Anda telah melakukan hal berikut:
Untuk menghitung estimasi kehadiran δ menggunakan Perlindungan Data Sensitif, kirim permintaan ke URL berikut, dengan PROJECT_ID menunjukkan project
ID Anda:
quasiIds[]: Wajib diisi. Kolom
(objek QuasiId) yang dianggap sebagai kuasi-pengidentifikasi untuk dipindai dan digunakan untuk menghitung
kehadiran δ. Tidak ada dua kolom yang dapat memiliki tag yang sama. Nilainya dapat berupa salah satu dari
berikut:
infoType: Hal ini menyebabkan Sensitive Data Protection menggunakan set data publik yang relevan sebagai model statistik populasi, termasuk kode ZIP AS, kode wilayah, usia, dan gender.
infoType kustom: Tag kustom tempat Anda menunjukkan tabel tambahan (objek
AuxiliaryTable) yang berisi informasi statistik tentang kemungkinan nilai kolom ini.
Tag inferred: Jika tidak ada tag semantik yang ditunjukkan, tentukan inferred.
Perlindungan Data Sensitif menyimpulkan model statistik dari distribusi nilai dalam data input.
regionCode: Kode wilayah alpha-2 ISO 3166-1
yang akan digunakan Sensitive Data Protection dalam pemodelan statistik. Nilai ini
wajib diisi jika tidak ada kolom yang diberi tag dengan infoType khusus wilayah (misalnya, kode pos AS) atau kode wilayah.
auxiliaryTables[]: Tabel tambahan
(objek StatisticalTable) yang akan digunakan dalam analisis. Setiap tag kustom yang digunakan untuk memberi tag pada kolom kuasi-ID (dari quasiIds[]) harus muncul di tepat satu kolom dari satu tabel tambahan.
Objek BigQueryTable. Tentukan tabel BigQuery yang akan dipindai dengan menyertakan semua hal berikut:
projectId: Project ID project yang berisi tabel.
datasetId: ID set data tabel.
tableId: Nama tabel.
Kumpulan satu atau beberapa objek
Action, yang merepresentasikan tindakan yang akan dijalankan, dalam urutan yang diberikan, setelah
penyelesaian tugas. Setiap objek Action dapat berisi salah satu
tindakan berikut:
SaveFindings
object: Menyimpan hasil pemindaian analisis risiko ke tabel BigQuery.
Untuk mengambil hasil tugas analisis risiko kehadiran δ menggunakan REST
API, kirim permintaan GET berikut ke resource
projects.dlpJobs. Ganti PROJECT_ID dengan project ID Anda dan
JOB_ID dengan ID tugas yang ingin Anda peroleh hasilnya.
ID pekerjaan ditampilkan saat Anda memulai pekerjaan, dan juga dapat diambil dengan
mencantumkan semua pekerjaan.
GET https://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs/JOB_ID
Permintaan menampilkan objek JSON yang berisi instance tugas. Hasil
analisis berada di dalam kunci "riskDetails", dalam objek
AnalyzeDataSourceRiskDetails. Untuk informasi selengkapnya, lihat referensi API untuk resource
DlpJob.
Langkah berikutnya
Pelajari cara menghitung nilai k-anonymity
untuk set data.
Pelajari cara menghitung nilai l-diversity
untuk set data.
Pelajari cara menghitung nilai k-map untuk set data.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-19 UTC."],[],[],null,["# Computing δ-presence for a dataset\n\nDelta-presence (*δ* -presence) is a metric that quantifies the probability that\nan individual belongs to an analyzed dataset. Like [*k*-map](#compute-k-map),\nyou can estimate *δ*-presence values using Sensitive Data Protection, which\nuses a statistical model to estimate the attack dataset.\n\n*δ*-presence contrasts with the other risk analysis methods, in which the\nattack dataset is explicitly known. Depending on the type of data,\nSensitive Data Protection uses publicly available datasets (for example, from the\nUS Census) or a custom statistical model (for example, one or more\nBigQuery tables that you specify), or it extrapolates from the\ndistribution of values in your input dataset.\n\nThis topic demonstrates how to compute *δ* -presence values for a dataset using\nSensitive Data Protection. For more information about *δ* -presence or risk analysis in\ngeneral, see the [risk analysis concept topic](/sensitive-data-protection/docs/concepts-risk-analysis)\nbefore continuing on.\n| **Note:** At this time, you can only compute *δ* -presence values using the DLP API or Sensitive Data Protection-supported [client\n| libraries](/sensitive-data-protection/docs/libraries). Sensitive Data Protection in the Google Cloud console doesn't support computing *δ*-presence values.\n\n\u003cbr /\u003e\n\n| **Note:** Prematurely canceling an operation midway through a job still incurs costs for the portion of the job that was completed. For more information about billing, see [Sensitive Data Protection pricing](https://cloud.google.com/sensitive-data-protection/pricing).\n\n\u003cbr /\u003e\n\nBefore you begin\n----------------\n\n\nBefore continuing, be sure you've done the following:\n\n1. [Sign in](https://accounts.google.com/Login) to your Google Account.\n2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.\n[Go to the project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n3. Make sure that billing is enabled for your Google Cloud project. [Learn how to confirm billing is enabled for your\n project.](/billing/docs/how-to/modify-project)\n4. Enable Sensitive Data Protection.\n[Enable Sensitive Data Protection](https://console.cloud.google.com/flows/enableapi?apiid=dlp.googleapis.com)\n5. Select a BigQuery dataset to analyze. Sensitive Data Protection estimates the *δ*-presence metric by scanning a BigQuery table.\n6. Determine the types of datasets you want to use to model the attack dataset. For more information, see the reference page for the [`DeltaPresenceEstimationConfig`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs#deltapresenceestimationconfig) object, as well as [Risk\n analysis terms and techniques](/sensitive-data-protection/docs/concepts-risk-analysis#risk_analysis_terms_and_techniques).\n\n\u003cbr /\u003e\n\nCompute *δ*-presence metrics\n----------------------------\n\nTo compute a *δ* -presence estimate using Sensitive Data Protection, send a request\nto the following URL, where \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e indicates your [project\nidentifier](https://cloud.google.com/resource-manager/docs/creating-managing-projects#identifying_projects): \n\n```\nhttps://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs\n```\n\nThe request contains a\n[`RiskAnalysisJobConfig`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs/create#riskanalysisjobconfig)\nobject, which is composed of the following:\n\n- A\n [`PrivacyMetric`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs#privacymetric)\n object. This is where you specify that you want to calculate *δ* -presence by\n specifying a\n [`DeltaPresenceEstimationConfig`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs#deltapresenceestimationconfig)\n object containing the following:\n\n - `quasiIds[]`: Required. Fields\n ([`QuasiId`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs#quasiid)\n objects) considered to be quasi-identifiers to scan and use to compute\n *δ*-presence. No two columns can have the same tag. These can be any of the\n following:\n\n - An [infoType](/sensitive-data-protection/docs/reference/rest/v2/InfoType): This causes Sensitive Data Protection to use the relevant public dataset as a statistical model of population, including US ZIP codes, region codes, ages, and genders.\n - A custom infoType: A custom tag wherein you indicate an auxiliary table (an [`AuxiliaryTable`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs#DlpJob.AuxiliaryTable) object) that contains statistical information about the possible values of this column.\n - The `inferred` tag: If no semantic tag is indicated, specify `inferred`. Sensitive Data Protection infers the statistical model from the distribution of values in the input data.\n - `regionCode`: An\n [ISO 3166-1 alpha-2 region code](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2)\n for Sensitive Data Protection to use in statistical modeling. This value\n is required if no column is tagged with a region-specific infoType (for\n example, a US ZIP code) or a region code.\n\n - `auxiliaryTables[]`: Auxiliary tables\n ([`StatisticalTable`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs#statisticaltable)\n objects) to use in the analysis. Each custom tag used to tag a\n quasi-identifier column (from `quasiIds[]`) must appear in exactly one\n column of one auxiliary table.\n\n- A [`BigQueryTable`](/sensitive-data-protection/docs/reference/rest/v2/BigQueryTable)\n object. Specify the BigQuery table to scan by including all of\n the following:\n\n - `projectId`: The project ID of the project containing the table.\n - `datasetId`: The dataset ID of the table.\n - `tableId`: The name of the table.\n- A set of one or more\n [`Action`](/sensitive-data-protection/docs/reference/rest/v2/InspectJobConfig#Action)\n objects, which represent actions to run, in the order given, at the\n completion of the job. Each `Action` object can contain one of the\n following actions:\n\n - [`SaveFindings`](/sensitive-data-protection/docs/reference/rest/v2/InspectJobConfig#SaveFindings) object: Saves the results of the risk analysis scan to a BigQuery table.\n - [`PublishToPubSub`](/sensitive-data-protection/docs/reference/rest/v2/InspectJobConfig#PublishToPubSub) object: [Publishes a notification to a Pub/Sub topic](/pubsub/docs/publisher).\n\n | **Note:** If there are configuration or permission issues with the Pub/Sub topic, Sensitive Data Protection retries sending the Pub/Sub notification for up to two weeks. After two weeks, the notification is discarded.\n - [`PublishSummaryToCscc`](/sensitive-data-protection/docs/reference/rest/v2/InspectJobConfig#PublishSummaryToCscc) object: Saves a results summary to Security Command Center.\n - [`PublishFindingsToCloudDataCatalog`](/sensitive-data-protection/docs/reference/rest/v2/InspectJobConfig#PublishFindingsToCloudDataCatalog) object: Saves results to [Data Catalog](/sensitive-data-protection/docs/sending-results-to-dc).\n - [`JobNotificationEmails`](/sensitive-data-protection/docs/reference/rest/v2/InspectJobConfig#JobNotificationEmails) object: Sends you an email with results.\n - [`PublishToStackdriver`](/sensitive-data-protection/docs/reference/rest/v2/InspectJobConfig#PublishToStackdriver) object: Saves results to Google Cloud Observability.\n\nViewing *δ*-presence job results\n--------------------------------\n\nTo retrieve the results of the *δ* -presence risk analysis job using the REST\nAPI, send the following GET request to the\n[`projects.dlpJobs`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs/get)\nresource. Replace \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e with your project ID and\n\u003cvar translate=\"no\"\u003eJOB_ID\u003c/var\u003e with the identifier of the job you want to obtain results for.\nThe job ID was returned when you started the job, and can also be retrieved by\n[listing all jobs](#list-jobs). \n\n```\nGET https://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs/JOB_ID\n```\n\nThe request returns a JSON object containing an instance of the job. The results\nof the analysis are inside the `\"riskDetails\"` key, in an\n[`AnalyzeDataSourceRiskDetails`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs#DlpJob.AnalyzeDataSourceRiskDetails)\nobject. For more information, see the API reference for the\n[`DlpJob`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs#DlpJob)\nresource.\n\nWhat's next\n-----------\n\n- Learn how to calculate the [*k*-anonymity](/sensitive-data-protection/docs/compute-k-anonymity) value for a dataset.\n- Learn how to calculate the [*l*-diversity](/sensitive-data-protection/docs/compute-l-diversity) value for a dataset.\n- Learn how to calculate the [*k*-map](/sensitive-data-protection/docs/compute-k-map) value for a dataset."]]
