이제 Cloud Data Loss Prevention(Cloud DLP)은 민감한 정보 보호에 포함됩니다. API 이름은 Cloud Data Loss Prevention API(DLP API)로 그대로 유지됩니다. 민감한 정보 보호를 구성하는 서비스에 대한 자세한 내용은 민감한 정보 보호 개요를 참조하세요.
민감한 정보 보호를 사용하여 데이터 세트의 k-익명성 값을 계산한 후 Looker Studio에서 결과를 시각화할 수 있습니다. 이렇게 하면 재식별 위험을 더 정확히 파악하고 데이터를 수정 또는 익명화하는 경우 감수해야 하는 유용성 측면의 타협을 평가하는 데 도움이 됩니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-04(UTC)"],[],[],null,["# Measuring re-identification and disclosure risk\n\n*Re-identification risk analysis* , or just *risk analysis*, is the process of\nanalyzing sensitive data to find properties that might increase the risk of\nsubjects being identified. You can use risk analysis methods before\nde-identification to help determine an effective de-identification strategy or\nafter de-identification to monitor for any changes or outliers.\n\nSensitive Data Protection can compute four re-identification risk metrics: *k* -anonymity,\n*l* -diversity, *k* -map, and *δ* -presence. If you're not familiar with risk\nanalysis or these metrics, see the [risk analysis concept\ntopic](/sensitive-data-protection/docs/concepts-risk-analysis) before continuing on.\n\nThis section provides overviews of how to use Sensitive Data Protection for risk\nanalysis of structured data using any of these metrics, plus other related\ntopics.\n\nCalculate re-identification risk\n--------------------------------\n\nSensitive Data Protection can analyze your structured data stored in\nBigQuery tables and compute the following re-identification risk\nmetrics. Click the link for the metric you want to calculate to learn more.\n\nCalculate other statistics\n--------------------------\n\nSensitive Data Protection can also compute numerical and categorical\nstatistics for data stored in BigQuery tables using the same\n[`DlpJob`](/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs) resource as the\nrisk analysis APIs.\n\nFor more information, see\n[Computing numerical and categorical statistics](/sensitive-data-protection/docs/compute-stats).\n\nVisualize re-identification risk\n--------------------------------\n\nYou can visualize the risk metrics that Sensitive Data Protection calculates\ndirectly in the Google Cloud console using Sensitive Data Protection\n([*k*-anonymity](/sensitive-data-protection/docs/compute-k-anonymity#viewing-results) or\n[*l*-diversity](/sensitive-data-protection/docs/compute-l-diversity#viewing-results)), or using other\nGoogle Cloud products."]]
