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Introducing a New Privacy Testing Library in TensorFlow — The TensorFlow Blog

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src='https://cdn.jsdelivr.net/npm/zoom-vanilla.js/dist/zoom-vanilla.min.js' type='text/javascript'></script> <!-- End Image Zoom--> <link href='https://www.gstatic.com/tf_blog/images/favicon.png' rel='shortcut icon' type='image/png'/> <script type='text/javascript'> //<![CDATA[ const qs = (string, el = document) => el.querySelector(string); const qsa = (string, el = document) => el.querySelectorAll(string); class App { constructor() { this.body = qs('body'); this.detailBody = qs('.tensorsite-detail__body'); this.overlay = qs('.header__overlay'); this.hamburger = qs('.header__hamburger'); this.sideMenu = qs('.header__side-menu'); this.detailBodies = qsa('.tensorsite-detail__body'); this.searchForms = qsa('.searchbox'); this.searchInputs = qsa('.search-input'); this.homeHref = qs('#home-href'); this.featuredCard = qs('.tensorsite-card.featured'); this.featuredPostHref = this.featuredCard && this.featuredCard .querySelector('.tensorsite-card__href') .getAttribute('href'); this.cards = qsa('.tensorsite-card'); this.images = qsa('img[border]'); this.cardDescriptions = qsa('.tensorsite-content__description'); this.hiddenDescription = qsa('.tensorsite-detail__description'); this.iconLinks = qs('.social-icons__links').children this.iconTooltips = qsa('[class^="icon-tooltip"]') this._toggleMobileMenu = this._toggleMobileMenu.bind(this); this._closeMenu = this._closeMenu.bind(this); this._onResize = this._onResize.bind(this); this._getScreen = this._getScreen.bind(this); this._searchGoogle = this._searchGoogle.bind(this); this._handleSearchKeypress = this._handleSearchKeypress.bind(this); this._removeDividerAboveImage(); this._setAllTagActive(); this._showFeaturedPost(); this._redirectWithMaxResults(); this._makeImagesZoomable(); this._removeCardLineBreaks(); this._getNextPost().then(()=>{ this._removeCardLineBreaks(); }) this.addEventListeners(); } addEventListeners() { window.addEventListener('resize', this._onResize); this.hamburger.addEventListener('click', this._toggleMobileMenu); this.searchForms.forEach(el => el.addEventListener('submit', this._searchGoogle)); this.searchInputs.forEach(el => el.addEventListener('keypress', this._handleSearchKeypress)); Array.from(this.iconLinks).forEach((icon, i) => { icon.addEventListener("mouseover", () => icon.querySelectorAll('[class^="icon-tooltip"]')[0].style.display = 'block'); icon.addEventListener("mouseout", () => icon.querySelectorAll('[class^="icon-tooltip"]')[0].style.display = 'none'); }) } _getNextPost() { return new Promise((resolve) => { const nextHref = qs('.tensorsite-detail__next-url'); if (this.detailBody && nextHref) { let request = new XMLHttpRequest(); request.open('GET', nextHref.getAttribute('href'), true); request.onload = function() { if (this.status >= 200 && this.status < 400) { // Success! Should be an HTML response // Save html in variable so you're able to query select const parser = new DOMParser(); const html = parser.parseFromString(this.response, "text/html"); const nextTitle = html.querySelector('.tensorsite-detail__title'); const nextDesc = html.querySelector('.tensorsite-detail__description'); const nextTags = html.querySelector('.tensorsite-detail__tags'); const nextHref = qs('.tensorsite-detail__next-url').getAttribute('href'); const nextImgUrl = html.querySelector('.tensorsite-detail__main-image'); const nextTitleEl = document.querySelector('.tensorsite-content__title.next'); const nextDescEl = document.querySelector('.tensorsite-content__description.next'); let nextTagsEl = document.querySelector('.tensorsite-content__subtitle.next'); const nextHrefEl = document.querySelector('.tensorsite-card__href.next'); const nextImgEl = document.querySelector('.tensorsite-content__image-wrapper'); const nextContainer = qs('.tensorsite-next'); const footer = qs('.tensorsite-footer'); if (nextTitleEl && nextTitle) { nextTitleEl.innerHTML = nextTitle.innerHTML; } if (nextDescEl && nextDesc) { nextDescEl.innerHTML = nextDesc.innerHTML; } if (nextTagsEl && nextTags) { nextTagsEl.innerHTML = nextTags.innerHTML; } if (nextHref && nextHrefEl) { nextHrefEl.setAttribute('href', nextHref); } if (nextImgEl && nextImgUrl) { // If Blogger can't find a firstImageUrl, it returns a // message informing us of that, so this checks // if the string is a URL if(!/http/.test(nextImgUrl.innerHTML)){ nextImgEl.classList.add('hidden'); } else { nextImgEl.querySelector('img').src = nextImgUrl.innerHTML; } } if (nextHref) { nextContainer.classList.add('active'); footer.classList.add('grey'); } resolve(); } else { // We reached our target server, but it returned an error console.error('Error: Could not get the next title'); } }; request.send(); } }) } get isMenuOpen() { return this.sideMenu.classList.contains('is-open'); } _handleSearchKeypress(e) { if (e.which == 13) { this._searchGoogle(); } } _searchGoogle(e) { e.preventDefault(); const {value} = e.target.querySelector('.search-input'); window.location.href = 'https://www.google.com/search?q=site%3A' + window.location.hostname + '%20' + value; } _toggleMobileMenu() { this.body.classList.toggle('no-scroll'); this.overlay.classList.toggle('show'); this.sideMenu.classList.toggle('is-open'); if (this.isMenuOpen) { this.overlay.addEventListener('click', this._closeMenu); } else { this.overlay.removeEventListener('click', this._closeMenu); } } _closeMenu(e) { if (this.isMenuOpen) { this._toggleMobileMenu(); } } _onResize() { if (this._getScreen().width > 839 && this.isMenuOpen) { this._closeMenu(); } } _getScreen() { return { scrollY: window.scrollY, width: window.innerWidth, height: window.innerHeight, } }; _removeDividerAboveImage() { if (this.detailBody && this.detailBody.firstElementChild && this.detailBody.firstElementChild.querySelector('img')) { const firstDivider = qs('.divider'); 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</div> <p aria-hidden='true' class='tensorsite-detail__description' hidden='true'> <span class='tensorsite-content__info'> June 24, 2020 &#8212; </span> <i>Posted by <a href="https://shs037.github.io/">Shuang Song</a> and <a href="https://www.linkedin.com/in/dmarn/">David Marn</a></i><br>Today, we&#8217;re excited to announce a new experimental module in <a href="https://github.com/tensorflow/privacy">TensorFlow Privacy</a> (<a href="https://github.com/tensorflow/privacy/tree/master/tensorflow_privacy/privacy/membership_inference_attack">GitHub</a>) that allows developers to assess the privacy properties of their classification models. <br>Privacy is an emerging topic in the Machine Learning community. There aren&#8217;t canonical guidelines to produce a private model. There is a growing body of research showing that a machine &#8230; </p> <div class='tensorsite-content__subtitle'> <a href='https://blog.tensorflow.org/search?label=TensorFlow+Core&max-results=20'> <span>TensorFlow Core</span> </a> <b class='label-divider-dot'>&#183;</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-detail__title'> Introducing a New Privacy Testing Library in TensorFlow </div> <div class='tensorsite-detail__contact'> <div class='tensorsite-detail__info'> <span class='tensorsite-detail__timestamp'>June 24, 2020</span> </div> <a class='icon-link' href='https://twitter.com/intent/tweet?text=%22Introducing a New Privacy Testing Library in TensorFlow%22 from the TensorFlow Blog%0A%0Ahttps://blog.tensorflow.org/2020/06/introducing-new-privacy-testing-library.html' rel='noopener noreferrer' target='_blank' title='Share this post on Twitter'> <svg alt='Twitter Social Icon' class='twitter-icon social-icon' height='19' viewBox='0 0 23 19' width='23' xmlns='http://www.w3.org/2000/svg'> <g fill='none' fill-rule='evenodd' transform='translate(-7 -9)'> <rect height='36' width='36'></rect> <path d='M14.076,27.2827953 C22.566,27.2827953 27.21,20.2477953 27.21,14.1477953 C27.21,13.9477953 27.21,13.7487953 27.197,13.5507953 C28.1,12.8977953 28.88,12.0887953 29.5,11.1617953 C28.657,11.5347953 27.764,11.7797953 26.848,11.8877953 C27.812,11.3107953 28.533,10.4037953 28.878,9.33479527 C27.972,9.87179527 26.98,10.2507953 25.947,10.4547953 C24.198,8.59579527 21.274,8.50679527 19.415,10.2547953 C18.217,11.3817953 17.708,13.0617953 18.08,14.6647953 C14.368,14.4787953 10.91,12.7257953 8.566,9.84279527 C7.341,11.9507953 7.967,14.6497953 9.995,16.0047953 C9.261,15.9827953 8.542,15.7837953 7.9,15.4267953 L7.9,15.4847953 C7.9,17.6827953 9.449,19.5747953 11.603,20.0107953 C10.924,20.1957953 10.211,20.2227953 9.519,20.0897953 C10.124,21.9707953 11.856,23.2587953 13.832,23.2957953 C12.197,24.5797953 10.178,25.2777953 8.098,25.2747953 C7.731,25.2747953 7.364,25.2527953 7,25.2087953 C9.111,26.5627953 11.567,27.2817953 14.076,27.2787953' fill='#545454'></path> </g> </svg> </a> </div> <div class='divider divider--article-top'></div> <div class='tensorsite-detail__body'> <i>Posted by <a href="https://shs037.github.io/">Shuang Song</a> and <a href="https://www.linkedin.com/in/dmarn/">David Marn</a></i> <a name='more'></a><br /> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEDjeoKAAuDrHuNF_D2azfZyW5-gr9dtXOIWERIG3RUtSO4mdFssafK5r1hCXEGfUB06ebpIsFenERTH1qKCoFfIaIYJZKHd32opYTW0YRTpg60fh3n8Y-1wtNdS7q7B_wZYv8W4i74iE/s1600/membership.png" imageanchor="1" ><img border="0" data-original-height="442" data-original-width="1023" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEDjeoKAAuDrHuNF_D2azfZyW5-gr9dtXOIWERIG3RUtSO4mdFssafK5r1hCXEGfUB06ebpIsFenERTH1qKCoFfIaIYJZKHd32opYTW0YRTpg60fh3n8Y-1wtNdS7q7B_wZYv8W4i74iE/s1600/membership.png" /></a></td></tr> <tr><td class="tr-caption" style="text-align: center;">Overview of a membership inference attack. An attacker tries to figure out whether certain examples were part of the training data.</td></tr> </tbody></table><p>Today, we&#8217;re excited to announce a new experimental module in <a href="https://github.com/tensorflow/privacy">TensorFlow Privacy</a> (<a href="https://github.com/tensorflow/privacy/tree/master/tensorflow_privacy/privacy/membership_inference_attack">GitHub</a>) that allows developers to assess the privacy properties of their classification models. <br /> </p><p>Privacy is an emerging topic in the Machine Learning community. There aren&#8217;t canonical guidelines to produce a private model. There is a growing body of research showing that a machine learning model can leak sensitive information of the training dataset, thus creating a privacy risk for users in the training set. <br /> </p><p>Last year, we launched TensorFlow Privacy, enabling developers to train their models with <a href="https://developers.googleblog.com/2019/09/enabling-developers-and-organizations.html">differential privacy</a>. Differential privacy adds noise to hide individual examples in the training dataset. However, this noise is designed for academic worst-case scenarios and can significantly affect model accuracy.<br /> </p><p>These challenges led us to tackle privacy from a different perspective. A few years ago, research around the privacy properties of machine learning models started to <a href="https://www.cs.cornell.edu/~shmat/shmat_oak17.pdf">emerge</a>. Cost-efficient &#8220;membership inference attacks&#8221; predict whether a specific piece of data was used during training. If an attacker is able to make a prediction with high accuracy, they will likely succeed in figuring out if a data piece was used in the training set. The biggest advantage of a membership inference attack is that it is easy to perform, i.e., does not require any re-training. <br /> </p><p>A test produces a vulnerability score that determines whether the model leaks information from the training set. We found that this vulnerability score often decreases with heuristics, such as early stopping or using <a href="https://arxiv.org/abs/1607.00133">DP-SGD</a> for training. <br /> </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><center><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiDbj6bBr4BHF1HQuQQ4NuMsPt1YTzpHysDrnm3WvqbQyDIJIXs1ZCgK6FrPllKGb2zBaSFwo2hRO9Ar9339fS3VoR2DUxPuUwyhOita4en_ZgjcYzhc7aiLojb-baF8kVjfUnSOJI-03U/s1600/cifar-blogpost.png" imageanchor="1" ><img border="0" data-original-height="606" data-original-width="608" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiDbj6bBr4BHF1HQuQQ4NuMsPt1YTzpHysDrnm3WvqbQyDIJIXs1ZCgK6FrPllKGb2zBaSFwo2hRO9Ar9339fS3VoR2DUxPuUwyhOita4en_ZgjcYzhc7aiLojb-baF8kVjfUnSOJI-03U/s1600/cifar-blogpost.png" style="width:400px" /></a></center></td></tr><tr><td class="tr-caption" style="text-align: center;">Membership inference attack on models for CIFAR10. The x-axis is the test accuracy of the model, and y-axis is vulnerability score (lower means more private). Vulnerability grows while test accuracy remains the same - better generalization could prevent privacy leakage.</td></tr></tbody></table>Unsurprisingly, differential privacy helps in reducing these vulnerability scores. Even with very small amounts of noise, the vulnerability score decreased. <br /> <br /> After using membership inference tests internally, we're sharing them with developers to help them build more private models, explore better architecture choices, use regularization techniques such as early stopping, dropout, weight decay, and input augmentation, or collect more data. Ultimately, these tests can help the developer community identify more architectures that incorporate privacy design principles and data processing choices.<br /> <br /> We hope this library will be the starting point of a robust privacy testing suite that can be used by any machine learning developer around the world. Moving forward, we&#8217;ll explore the feasibility of extending membership inference attacks beyond classifiers and develop new tests. We&#8217;ll also explore adding this test to the TensorFlow ecosystem by integrating with <a href="https://www.tensorflow.org/tfx">TFX</a>.<br /> <br /> Reach out to <a href="mailto:tf-privacy@google.com">tf-privacy@google.com</a> and let us know how you&#8217;re using this new module. We&#8217;re keen on hearing your stories, feedback, and suggestions!<br /> <br /> <i>Acknowledgments: Yurii Sushko, Andreas Terzis, Miguel Guevara, Niki Kilbertus, Vadym Doroshenko, Borja De Balle Pigem, Ananth Raghunathan. </i> </div> </div> <div class='tensorsite-detail-footer'> <div class='article-divider'> <img alt='Diamond Article Divider' src='https://www.gstatic.com/tf_blog/images/ic_article_end.svg'/> </div> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=TensorFlow+Core&max-results=20'> TensorFlow Core </a> </div> </div> <div class='tensorsite-next'> <div class='tensorsite-container--large'> <div class='tensorsite-next__title'>Next post</div> <div class='tensorsite-card'> <a aria-label='Next Card' class='tensorsite-card__href next' href='https://blog.tensorflow.org/2020/06/introducing-new-privacy-testing-library.html'></a> <div class='tensorsite-content__image-wrapper'> <img alt='Introducing a New Privacy Testing Library in TensorFlow' class='tensorsite-content__image' src='https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEDjeoKAAuDrHuNF_D2azfZyW5-gr9dtXOIWERIG3RUtSO4mdFssafK5r1hCXEGfUB06ebpIsFenERTH1qKCoFfIaIYJZKHd32opYTW0YRTpg60fh3n8Y-1wtNdS7q7B_wZYv8W4i74iE/s1600/membership.png'/> </div> <div class='tensorsite-content'> <div class='tensorsite-content__subtitle next'> <span>TensorFlow Core</span> <b class='label-divider-dot'>&#183;</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-content__title next'> Introducing a New Privacy Testing Library in TensorFlow </div> <p class='tensorsite-content__description next'> <span class='tensorsite-content__info'> June 24, 2020 </span> &#8212; <span> <i>Posted by <a href="https://shs037.github.io/">Shuang Song</a> and <a href="https://www.linkedin.com/in/dmarn/">David Marn</a></i><br>Today, we&#8217;re excited to announce a new experimental module in <a href="https://github.com/tensorflow/privacy">TensorFlow Privacy</a> (<a href="https://github.com/tensorflow/privacy/tree/master/tensorflow_privacy/privacy/membership_inference_attack">GitHub</a>) that allows developers to assess the privacy properties of their classification models. <br>Privacy is an emerging topic in the Machine Learning community. There aren&#8217;t canonical guidelines to produce a private model. 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