機密データの保護を使用して機密データを把握、管理、保護できます。Sensitive Data Protection を使用すると、テキストベースのコンテンツや画像（Google Cloud Storage リポジトリに保存されているコンテンツを含む）の中にある機密データを簡単に分類して削除できます。
テキスト分類
次のテキスト入力があるとします。
Please update my records with the following information:
Email address: foo@example.com
National Provider Identifier: 1245319599
Driver's license: AC333991
Please update my records with the following information:
Email address: foo@example.com
National Provider Identifier: 1245319599
Driver's license: AC333991
プレースホルダ "***" を使用した出力例:
Please update my records with the following information:
Email address: ***
National Provider Identifier: ***
Driver's license: ***
画像分類
機密データの保護では、分類前のテキストの解読に OCR（Optical Character Recognition: 光学式文字認識）技術を使用します。テキスト分類と同様に見つかったテキストが返されますが、そのテキストが見つかった境界ボックスも追加されます。
[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-01-31 UTC。"],[],[],null,["# Classification, redaction, and de-identification\n\nThe Sensitive Data Protection helps you understand, manage, and protect sensitive data.\nWith the Sensitive Data Protection, you can easily classify and redact sensitive\ndata contained in text-based content and images, including content stored in\nGoogle Cloud storage repositories.\n\nText classification\n-------------------\n\nGiven the following text input: \n\n```\nPlease update my records with the following information:\nEmail address: foo@example.com\n\nNational Provider Identifier: 1245319599\n\nDriver's license: AC333991\n```\n\nThe output is a list of findings, organized into the following categories:\n\n- [`InfoType`](/sensitive-data-protection/docs/infotypes-reference \"InfoTypes Reference\")\n- [`Likelihood`](/sensitive-data-protection/docs/likelihood \"Likelihood Concept Page\")\n- `Offset` (Where in the string the potential `InfoType` was found)\n\nExample output is shown in the table below.\n\nAutomatic text redaction\n------------------------\n\nAutomatic redaction produces an output with sensitive data matches removed\ninstead of giving you a list of findings.\n\nExample automation redaction input: \n\n```\nPlease update my records with the following information:\nEmail address: foo@example.com\n\nNational Provider Identifier: 1245319599\n\nDriver's license: AC333991\n```\n\nExample output using a placeholder of \"\\*\\*\\*\": \n\n```\nPlease update my records with the following information:\nEmail address: ***\n\nNational Provider Identifier: ***\n\nDriver's license: ***\n```\n\nImage classification\n--------------------\n\nSensitive Data Protection uses Optical Character Recognition (OCR)\ntechnology to recognize text prior to classification. Similar to text\nclassification, it returns findings, but it also adds a bounding box\nwhere the text was found.\n\nStorage classification\n----------------------\n\nStorage classification scans data stored in Cloud Storage, Firestore in Datastore mode (Datastore),\nand BigQuery. Instead of streaming data into Sensitive Data Protection, you\nspecify in your request the storage location for the Cloud Storage\nbucket, Datastore kind, or BigQuery table you\nwant Sensitive Data Protection to scan.\n\nWhen scanning files in Cloud Storage locations, Sensitive Data Protection\nsupports scanning of binary, text, image, Microsoft Word, Microsoft Excel,\nMicrosoft Powerpoint, PDF, and Apache Avro\nfiles. A list of file extensions for the file types within Cloud Storage\nthat Sensitive Data Protection can scan is available on the API reference page for\n[`FileType`](/sensitive-data-protection/docs/reference/rest/v2/FileType).\nFiles of types that are unrecognized are scanned as binary files.\n\nThe results of the scan can be either saved to a new BigQuery\ntable or published to a Pub/Sub topic. From there, you can use\nbuilt-in BigQuery tools to run rich SQL analytics or tools\nsuch as Looker Studio to generate reports.\n\nFor more information about scanning storage repositories for sensitive data\nusing Sensitive Data Protection, see [Inspecting storage and databases for\nsensitive data](/sensitive-data-protection/docs/inspecting-storage).\n\nFor more information about visualizing scan results using other Google Cloud\ntools, see [Analyzing and reporting on Sensitive Data Protection\nfindings](/sensitive-data-protection/docs/analyzing-and-reporting).\n\nWhat's next\n-----------\n\n- Learn more about [image inspection and redaction](/sensitive-data-protection/docs/concepts-image-redaction).\n- Learn about [transformation methods](/sensitive-data-protection/docs/transformations-reference) that you can use with Sensitive Data Protection.\n- Work through the [Redacting Sensitive Data with Sensitive Data Protection](https://www.cloudskillsboost.google/focuses/46234?parent=catalog) codelab.\n- Learn more about [creating a de-identified copy of data in\n storage](/sensitive-data-protection/docs/concepts-deidentify-storage)."]]
