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<div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">We are excited to announce that differential privacy enforcement with privacy budgeting is now available in </span><a href="https://cloud.google.com/bigquery/docs/data-clean-rooms"><span style="text-decoration: underline; vertical-align: baseline;">BigQuery data clean rooms</span></a><span style="vertical-align: baseline;"> to help organizations prevent data from being reidentified when it is shared.</span></p>
<p><span style="vertical-align: baseline;">Differential privacy is an anonymization technique that limits the personal information that is revealed in a query output. Differential privacy is considered to be one of the strongest privacy protections that exists today because it:</span></p>
<ul>
<li role="presentation"><span style="vertical-align: baseline;">is provably private</span></li>
<li role="presentation"><span style="vertical-align: baseline;">supports multiple differentially private queries on the same dataset</span></li>
<li role="presentation"><span style="vertical-align: baseline;">can be applied to many data types</span></li>
</ul>
<p><span style="vertical-align: baseline;">Differential privacy is </span><a href="https://iabtechlab.com/wp-content/uploads/2023/11/Differential-Privacy-Guidance_PUBLIC-COMMENT_11152023.pdf" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">used by advertisers</span></a><span style="vertical-align: baseline;">, healthcare companies, and education companies to perform analysis without exposing individual records. It is also used by public sector organizations that comply with the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), the Family Educational Rights and Privacy Act (FERPA), and the California Consumer Privacy Act (CCPA).</span></p>
<h3><span style="vertical-align: baseline;">What can I do with differential privacy?</span></h3>
<p><span style="vertical-align: baseline;">With differential privacy, you can:</span></p>
<ul>
<li role="presentation"><span style="vertical-align: baseline;">protect individual records from re-identification without moving or copying your data</span></li>
<li role="presentation"><span style="vertical-align: baseline;">protect against privacy leak and re-identification</span></li>
<li role="presentation"><span style="vertical-align: baseline;">use one of the anonymization standards most favored by regulators</span></li>
</ul>
<p><span style="vertical-align: baseline;">BigQuery customers can use differential privacy to:</span></p>
<ul>
<li role="presentation"><span style="vertical-align: baseline;">share data in BigQuery data clean rooms while preserving privacy</span></li>
<li role="presentation"><span style="vertical-align: baseline;">anonymize query results on AWS and Azure data with BigQuery Omni</span></li>
<li role="presentation"><span style="vertical-align: baseline;">share anonymized results with Apache Spark stored procedures and Dataform pipelines so they can be consumed by other applications</span></li>
<li role="presentation"><span style="vertical-align: baseline;">enhance differential privacy implementations with technology from Google Cloud partners </span><a href="http://gretel.ai" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Gretel.ai</span></a><span style="vertical-align: baseline;"> and </span><a href="https://www.tmlt.dev/" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Tumult Analytics</span></a></li>
<li role="presentation"><span style="vertical-align: baseline;">call frameworks like </span><a href="http://pipelinedp.io" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">PipelineDP.io</span></a></li>
</ul>
<h3><span style="vertical-align: baseline;">So what is BigQuery differential privacy exactly?</span></h3>
<p><span style="vertical-align: baseline;">BigQuery differential privacy is three capabilities:</span></p>
<ul>
<li aria-level="1" style="list-style-type: disc; vertical-align: baseline;">
<p role="presentation"><strong style="vertical-align: baseline;">Differential privacy in GoogleSQL</strong><span style="vertical-align: baseline;"> – You can use </span><a href="https://cloud.google.com/bigquery/docs/differential-privacy"><span style="text-decoration: underline; vertical-align: baseline;">differential privacy aggregate functions</span></a><span style="vertical-align: baseline;"> directly in GoogleSQL</span></p>
</li>
<li aria-level="1" style="list-style-type: disc; vertical-align: baseline;">
<p role="presentation"><strong style="vertical-align: baseline;">Differential privacy enforcement in BigQuery data clean rooms </strong><span style="vertical-align: baseline;">– You can apply a </span><a href="https://cloud.google.com/bigquery/docs/analysis-rules#dp_privacy_policy"><span style="text-decoration: underline; vertical-align: baseline;">differential privacy analysis rule</span></a><span style="vertical-align: baseline;"> to enforce that all queries on your shared data use differential privacy in GoogleSQL with the parameters that you specify</span></p>
</li>
<li role="presentation"><strong style="vertical-align: baseline;">Parameter-driven privacy budgeting in BigQuery data clean rooms</strong><span style="vertical-align: baseline;"> – When you apply a differential privacy analysis rule, you also set a privacy budget to limit the data that is revealed when your shared data is queried. BigQuery uses parameter-driven privacy budgeting to give you more granular control over your data than query thresholds do and to prevent further queries on that data when the budget is exhausted.</span></li>
</ul>
<h3><strong style="vertical-align: baseline;">BigQuery differential privacy enforcement in action</strong></h3>
<p><span style="vertical-align: baseline;">Here’s how to enable the differential privacy analysis rule and configure a privacy budget when you add data to a BigQuery data clean room.</span></p></div>
<div class="block-image_full_width">

</div>
    </div>

</div>
<div class="block-paragraph"><p data-block-key="s1awi">Subscribers of that clean room must then use differential privacy to query your shared data.</p></div>
<div class="block-image_full_width">

</div>
    </div>

</div>
<div class="block-paragraph"><p data-block-key="s1awi">Subscribers of that clean room cannot query your shared data once the privacy budget is exhausted.</p></div>
<div class="block-image_full_width">

</div>
    </div>

</div>
<div class="block-paragraph_advanced"><h3><strong style="vertical-align: baseline;">Get started with BigQuery differential privacy</strong></h3>
<p><span style="vertical-align: baseline;">BigQuery differential privacy is configured when a data owner or contributor shares data in a BigQuery data clean room. A data owner or contributor can share data using any compute pricing model and does not incur compute charges when a subscriber queries that data. Subscribers of a data clean room incur compute charges when querying shared data that is protected with a differential privacy analysis rule. Those subscribers are required to use on-demand pricing (charged per TB) or the </span><a href="https://cloud.google.com/bigquery/docs/editions-intro"><span style="text-decoration: underline; vertical-align: baseline;">Enterprise Plus edition</span></a><span style="vertical-align: baseline;"> (charged per slot hour).</span></p>
<p><span style="vertical-align: baseline;">Create a </span><a href="https://pantheon.corp.google.com/bigquery/analytics-hub/exchanges" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">clean room where all queries are protected with differential privacy </span></a><span style="vertical-align: baseline;">today and </span><a href="https://issuetracker.google.com/issues/new?component=187149&amp;template=1162659&amp;pli=1" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">let us know</span></a><span style="vertical-align: baseline;"> where you need help.</span></p></div>
<div class="block-related_article_tout">

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            <h4 class="uni-related-article-tout__header h-has-bottom-margin">Privacy-preserving data sharing now generally available with BigQuery data clean rooms</h4>
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--- !ruby/object:Feedjira::Parser::RSSEntry
published: 2024-04-05 15:59:00.000000000 Z
carlessian_info:
  news_filer_version: 2
  newspaper: Google Cloud Blog
  macro_region: Technology
entry_id: !ruby/object:Feedjira::Parser::GloballyUniqueIdentifier
  guid: https://cloud.google.com/blog/products/data-analytics/differential-privacy-enforcement-in-bigquery-data-clean-rooms/
title: Get started with differential privacy and privacy budgeting in BigQuery data
  clean rooms
categories:
- Data Analytics
summary: "<div class=\"block-paragraph_advanced\"><p><span style=\"vertical-align:
  baseline;\">We are excited to announce that differential privacy enforcement with
  privacy budgeting is now available in </span><a href=\"https://cloud.google.com/bigquery/docs/data-clean-rooms\"><span
  style=\"text-decoration: underline; vertical-align: baseline;\">BigQuery data clean
  rooms</span></a><span style=\"vertical-align: baseline;\"> to help organizations
  prevent data from being reidentified when it is shared.</span></p>\n<p><span style=\"vertical-align:
  baseline;\">Differential privacy is an anonymization technique that limits the personal
  information that is revealed in a query output. Differential privacy is considered
  to be one of the strongest privacy protections that exists today because it:</span></p>\n<ul>\n<li
  role=\"presentation\"><span style=\"vertical-align: baseline;\">is provably private</span></li>\n<li
  role=\"presentation\"><span style=\"vertical-align: baseline;\">supports multiple
  differentially private queries on the same dataset</span></li>\n<li role=\"presentation\"><span
  style=\"vertical-align: baseline;\">can be applied to many data types</span></li>\n</ul>\n<p><span
  style=\"vertical-align: baseline;\">Differential privacy is </span><a href=\"https://iabtechlab.com/wp-content/uploads/2023/11/Differential-Privacy-Guidance_PUBLIC-COMMENT_11152023.pdf\"
  rel=\"noopener\" target=\"_blank\"><span style=\"text-decoration: underline; vertical-align:
  baseline;\">used by advertisers</span></a><span style=\"vertical-align: baseline;\">,
  healthcare companies, and education companies to perform analysis without exposing
  individual records. It is also used by public sector organizations that comply with
  the General Data Protection Regulation (GDPR), the Health Insurance Portability
  and Accountability Act (HIPAA), the Family Educational Rights and Privacy Act (FERPA),
  and the California Consumer Privacy Act (CCPA).</span></p>\n<h3><span style=\"vertical-align:
  baseline;\">What can I do with differential privacy?</span></h3>\n<p><span style=\"vertical-align:
  baseline;\">With differential privacy, you can:</span></p>\n<ul>\n<li role=\"presentation\"><span
  style=\"vertical-align: baseline;\">protect individual records from re-identification
  without moving or copying your data</span></li>\n<li role=\"presentation\"><span
  style=\"vertical-align: baseline;\">protect against privacy leak and re-identification</span></li>\n<li
  role=\"presentation\"><span style=\"vertical-align: baseline;\">use one of the anonymization
  standards most favored by regulators</span></li>\n</ul>\n<p><span style=\"vertical-align:
  baseline;\">BigQuery customers can use differential privacy to:</span></p>\n<ul>\n<li
  role=\"presentation\"><span style=\"vertical-align: baseline;\">share data in BigQuery
  data clean rooms while preserving privacy</span></li>\n<li role=\"presentation\"><span
  style=\"vertical-align: baseline;\">anonymize query results on AWS and Azure data
  with BigQuery Omni</span></li>\n<li role=\"presentation\"><span style=\"vertical-align:
  baseline;\">share anonymized results with Apache Spark stored procedures and Dataform
  pipelines so they can be consumed by other applications</span></li>\n<li role=\"presentation\"><span
  style=\"vertical-align: baseline;\">enhance differential privacy implementations
  with technology from Google Cloud partners </span><a href=\"http://gretel.ai\" rel=\"noopener\"
  target=\"_blank\"><span style=\"text-decoration: underline; vertical-align: baseline;\">Gretel.ai</span></a><span
  style=\"vertical-align: baseline;\"> and </span><a href=\"https://www.tmlt.dev/\"
  rel=\"noopener\" target=\"_blank\"><span style=\"text-decoration: underline; vertical-align:
  baseline;\">Tumult Analytics</span></a></li>\n<li role=\"presentation\"><span style=\"vertical-align:
  baseline;\">call frameworks like </span><a href=\"http://pipelinedp.io\" rel=\"noopener\"
  target=\"_blank\"><span style=\"text-decoration: underline; vertical-align: baseline;\">PipelineDP.io</span></a></li>\n</ul>\n<h3><span
  style=\"vertical-align: baseline;\">So what is BigQuery differential privacy exactly?</span></h3>\n<p><span
  style=\"vertical-align: baseline;\">BigQuery differential privacy is three capabilities:</span></p>\n<ul>\n<li
  aria-level=\"1\" style=\"list-style-type: disc; vertical-align: baseline;\">\n<p
  role=\"presentation\"><strong style=\"vertical-align: baseline;\">Differential privacy
  in GoogleSQL</strong><span style=\"vertical-align: baseline;\"> – You can use </span><a
  href=\"https://cloud.google.com/bigquery/docs/differential-privacy\"><span style=\"text-decoration:
  underline; vertical-align: baseline;\">differential privacy aggregate functions</span></a><span
  style=\"vertical-align: baseline;\"> directly in GoogleSQL</span></p>\n</li>\n<li
  aria-level=\"1\" style=\"list-style-type: disc; vertical-align: baseline;\">\n<p
  role=\"presentation\"><strong style=\"vertical-align: baseline;\">Differential privacy
  enforcement in BigQuery data clean rooms </strong><span style=\"vertical-align:
  baseline;\">– You can apply a </span><a href=\"https://cloud.google.com/bigquery/docs/analysis-rules#dp_privacy_policy\"><span
  style=\"text-decoration: underline; vertical-align: baseline;\">differential privacy
  analysis rule</span></a><span style=\"vertical-align: baseline;\"> to enforce that
  all queries on your shared data use differential privacy in GoogleSQL with the parameters
  that you specify</span></p>\n</li>\n<li role=\"presentation\"><strong style=\"vertical-align:
  baseline;\">Parameter-driven privacy budgeting in BigQuery data clean rooms</strong><span
  style=\"vertical-align: baseline;\"> – When you apply a differential privacy analysis
  rule, you also set a privacy budget to limit the data that is revealed when your
  shared data is queried. BigQuery uses parameter-driven privacy budgeting to give
  you more granular control over your data than query thresholds do and to prevent
  further queries on that data when the budget is exhausted.</span></li>\n</ul>\n<h3><strong
  style=\"vertical-align: baseline;\">BigQuery differential privacy enforcement in
  action</strong></h3>\n<p><span style=\"vertical-align: baseline;\">Here’s how to
  enable the differential privacy analysis rule and configure a privacy budget when
  you add data to a BigQuery data clean room.</span></p></div>\n<div class=\"block-image_full_width\">\n\n\n\n\n\n\n
  \ \n    <div class=\"article-module h-c-page\">\n      <div class=\"h-c-grid\">\n
  \ \n\n    <figure class=\"article-image--large\n      \n      \n        h-c-grid__col\n
  \       h-c-grid__col--6 h-c-grid__col--offset-3\n        \n        \n      \"\n
  \     >\n\n      \n      \n        \n        <img\n            src=\"https://storage.googleapis.com/gweb-cloudblog-publish/images/figure_1.max-1000x1000.png\"\n
  \       \n          alt=\"figure 1\">\n        \n        </a>\n      \n    </figure>\n\n
  \ \n      </div>\n    </div>\n  \n\n\n\n\n</div>\n<div class=\"block-paragraph\"><p
  data-block-key=\"s1awi\">Subscribers of that clean room must then use differential
  privacy to query your shared data.</p></div>\n<div class=\"block-image_full_width\">\n\n\n\n\n\n\n
  \ \n    <div class=\"article-module h-c-page\">\n      <div class=\"h-c-grid\">\n
  \ \n\n    <figure class=\"article-image--large\n      \n      \n        h-c-grid__col\n
  \       h-c-grid__col--6 h-c-grid__col--offset-3\n        \n        \n      \"\n
  \     >\n\n      \n      \n        \n        <img\n            src=\"https://storage.googleapis.com/gweb-cloudblog-publish/images/2_NP4i0TM.max-1000x1000.png\"\n
  \       \n          alt=\"figure 2\">\n        \n        </a>\n      \n    </figure>\n\n
  \ \n      </div>\n    </div>\n  \n\n\n\n\n</div>\n<div class=\"block-paragraph\"><p
  data-block-key=\"s1awi\">Subscribers of that clean room cannot query your shared
  data once the privacy budget is exhausted.</p></div>\n<div class=\"block-image_full_width\">\n\n\n\n\n\n\n
  \ \n    <div class=\"article-module h-c-page\">\n      <div class=\"h-c-grid\">\n
  \ \n\n    <figure class=\"article-image--large\n      \n      \n        h-c-grid__col\n
  \       h-c-grid__col--6 h-c-grid__col--offset-3\n        \n        \n      \"\n
  \     >\n\n      \n      \n        \n        <img\n            src=\"https://storage.googleapis.com/gweb-cloudblog-publish/original_images/3_gO4XfWR.png\"\n
  \       \n          alt=\"figure 3\">\n        \n        </a>\n      \n    </figure>\n\n
  \ \n      </div>\n    </div>\n  \n\n\n\n\n</div>\n<div class=\"block-paragraph_advanced\"><h3><strong
  style=\"vertical-align: baseline;\">Get started with BigQuery differential privacy</strong></h3>\n<p><span
  style=\"vertical-align: baseline;\">BigQuery differential privacy is configured
  when a data owner or contributor shares data in a BigQuery data clean room. A data
  owner or contributor can share data using any compute pricing model and does not
  incur compute charges when a subscriber queries that data. Subscribers of a data
  clean room incur compute charges when querying shared data that is protected with
  a differential privacy analysis rule. Those subscribers are required to use on-demand
  pricing (charged per TB) or the </span><a href=\"https://cloud.google.com/bigquery/docs/editions-intro\"><span
  style=\"text-decoration: underline; vertical-align: baseline;\">Enterprise Plus
  edition</span></a><span style=\"vertical-align: baseline;\"> (charged per slot hour).</span></p>\n<p><span
  style=\"vertical-align: baseline;\">Create a </span><a href=\"https://pantheon.corp.google.com/bigquery/analytics-hub/exchanges\"
  rel=\"noopener\" target=\"_blank\"><span style=\"text-decoration: underline; vertical-align:
  baseline;\">clean room where all queries are protected with differential privacy
  </span></a><span style=\"vertical-align: baseline;\">today and </span><a href=\"https://issuetracker.google.com/issues/new?component=187149&amp;template=1162659&amp;pli=1\"
  rel=\"noopener\" target=\"_blank\"><span style=\"text-decoration: underline; vertical-align:
  baseline;\">let us know</span></a><span style=\"vertical-align: baseline;\"> where
  you need help.</span></p></div>\n<div class=\"block-related_article_tout\">\n\n\n\n\n\n<div
  class=\"uni-related-article-tout h-c-page\">\n  <section class=\"h-c-grid\">\n    <a
  href=\"https://cloud.google.com/blog/products/data-analytics/bigquery-data-clean-rooms-now-generally-available/\"\n
  \      data-analytics='{\n                       \"event\": \"page interaction\",\n
  \                      \"category\": \"article lead\",\n                       \"action\":
  \"related article - inline\",\n                       \"label\": \"article: {slug}\"\n
  \                    }'\n       class=\"uni-related-article-tout__wrapper h-c-grid__col
  h-c-grid__col--8 h-c-grid__col-m--6 h-c-grid__col-l--6\n        h-c-grid__col--offset-2
  h-c-grid__col-m--offset-3 h-c-grid__col-l--offset-3 uni-click-tracker\">\n      <div
  class=\"uni-related-article-tout__inner-wrapper\">\n        <p class=\"uni-related-article-tout__eyebrow
  h-c-eyebrow\">Related Article</p>\n\n        <div class=\"uni-related-article-tout__content-wrapper\">\n
  \         <div class=\"uni-related-article-tout__image-wrapper\">\n            <div
  class=\"uni-related-article-tout__image\" style=\"background-image: url('')\"></div>\n
  \         </div>\n          <div class=\"uni-related-article-tout__content\">\n
  \           <h4 class=\"uni-related-article-tout__header h-has-bottom-margin\">Privacy-preserving
  data sharing now generally available with BigQuery data clean rooms</h4>\n            <p
  class=\"uni-related-article-tout__body\">Now GA, BigQuery data clean rooms has a
  new data contributor and subscriber experience, join restrictions, new analysis
  rules, usage metr...</p>\n            <div class=\"cta module-cta h-c-copy  uni-related-article-tout__cta
  muted\">\n              <span class=\"nowrap\">Read Article\n                <svg
  class=\"icon h-c-icon\" role=\"presentation\">\n                  <use xmlns:xlink=\"http://www.w3.org/1999/xlink\"
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  \           </div>\n          </div>\n        </div>\n      </div>\n    </a>\n  </section>\n</div>\n\n</div>"
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url: https://cloud.google.com/blog/products/data-analytics/differential-privacy-enforcement-in-bigquery-data-clean-rooms/
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