1479 lines
110 KiB
Text
1479 lines
110 KiB
Text
<!DOCTYPE html>
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<html lang="en" dir="ltr" prefix="content: http://purl.org/rss/1.0/modules/content/ dc: http://purl.org/dc/terms/ foaf: http://xmlns.com/foaf/0.1/ og: http://ogp.me/ns# rdfs: http://www.w3.org/2000/01/rdf-schema# schema: http://schema.org/ sioc: http://rdfs.org/sioc/ns# sioct: http://rdfs.org/sioc/types# skos: http://www.w3.org/2004/02/skos/core# xsd: http://www.w3.org/2001/XMLSchema# ">
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<head>
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<h1 class="nist-page__title">Privacy Attacks in Federated Learning</h1>
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<div class="daterange"><time datetime="2024-01-24T12:00:00Z">January 24, 2024</time>
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<a href="/blogs/cybersecurity-insights/authors/joseph-near" hreflang="en">Joseph Near</a>, <a href="/blogs/cybersecurity-insights/authors/david-darais" hreflang="en">David Darais</a>, <a href="/blogs/cybersecurity-insights/authors/dave-buckley" hreflang="en">Dave Buckley</a> and <a href="/blogs/cybersecurity-insights/authors/mark-durkee" hreflang="en">Mark Durkee</a>
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<h5><span><span><span><span><em><span lang="EN"><span>This post is part of a series on privacy-preserving federated learning. The series is a collaboration between NIST and the UK government’s Centre for Data Ethics and Innovation. Learn more and read all the posts published to date at </span></span></em><a href="https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/blog-series/privacy-preserving"><em><span><span>NIST’s Privacy Engineering Collaboration Space</span></span></em></a><em><span lang="EN"><span> or </span></span></em><a href="https://cdei.blog.gov.uk/2023/12/07/the-uk-us-blog-series-on-privacy-preserving-federated-learning-introduction/"><em><span><span>the CDEI blog</span></span></em></a><em><span lang="EN"><span>.</span></span></em></span></span></span></span></h5>
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<p><span><span><span>Our <a href="https://www.nist.gov/blogs/cybersecurity-insights/uk-us-blog-series-privacy-preserving-federated-learning-introduction">first post</a> in the series introduced the concept of federated learning—an approach for training AI models on distributed data by sharing <em>model</em> <em>updates</em> instead of training data. At first glance, federated learning seems to be a perfect fit for privacy since it completely avoids sharing data.</span></span></span></p>
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<p><span><span><span>However, recent work on privacy attacks has shown that it’s possible to extract a surprising amount of information about the training data, <em>even when federated learning is used</em>. These techniques fall into two major categories: attacks that target the model updates shared during training, and attacks that extract data from the AI model after training has finished.</span></span></span></p>
|
||
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<p><span><span><span>This post summarizes known attacks and provides recent examples from the research literature. The primary goal of the <a href="https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/challenges/prize-challenges">UK-US PETs Prize Challenges</a> was to develop practical defenses that augment federated learning frameworks to prevent these attacks; future posts in this series will describe these defenses in detail.</span></span></span></p>
|
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<p><strong><span><span><span><span>Attacks on Model Updates</span></span></span></span></strong></p>
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<p><span><span><span>In federated learning, each participant submits <em>model updates</em> instead of raw training data during the training process. In our example from the last post - in which a consortium of banks wants to train an AI model to detect fraudulent transactions - the model updates may consist of updates to the model’s parameters (the components of the model that control how its predictions are made) rather than raw data about financial transactions. At first glance, the model updates may appear to convey no information about financial transactions.</span></span></span></p>
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<img alt=" Figure 1: Data extracted from model updates by the attack developed by Hitaj et al. [1]. The top row contains original training data; the bottom row contains data extracted from model updates." height="100" loading="lazy" src="/sites/default/files/styles/480_x_480_limit/public/images/2024/01/24/Privacy%20Blog%201_24_2024.png?itok=QkJUspqM" typeof="foaf:Image" width="212">
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<figcaption class="nist-image__caption">Figure 1: Data extracted from model updates by the attack developed by Hitaj et al. The top row contains original training data; the bottom row contains data extracted from model updates. Credit: Hitaj et al.<div class="nist-image__credit">
|
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<span class="nist-image__credit-label">Credit:</span>
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NIST
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<p><span><span><span>However, recent research has demonstrated that it’s often possible to extract raw training data from model updates. One early example came from <a href="https://dl.acm.org/doi/10.1145/3133956.3134012">the work of Hitaj et al.</a>, who showed that it was possible to train a <em>second</em> AI model to reconstruct training data based on model updates. One example of their results appears in Figure 1: the top row contains training data used to train a model that recognizes handwritten digits, and the bottom row contains <em>data extracted from model updates</em> by their attack.</span></span></span></p>
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<img alt="Data extracted from model updates by the attack developed by Zhu et al." height="246" loading="lazy" src="/sites/default/files/styles/480_x_480_limit/public/images/2024/01/24/Picture5.png?itok=ZOawVXKy" typeof="foaf:Image" width="390">
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<figcaption class="nist-image__caption">Figure 2: Data extracted from model updates by the attack developed by Zhu et al. Each row corresponds to a different training dataset and AI model. Each column shows data extracted from model updates during training; columns with higher values for “Iters” represent data extracted later in the training process. Credit: Zhu et al.<div class="nist-image__credit">
|
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<span class="nist-image__credit-label">Credit:</span>
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NIST
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<p><span><span><span><a href="https://arxiv.org/abs/1906.08935">Later work by Zhu et al.</a> suggests that this kind of attack is possible for many different kinds of models and their corresponding model updates. Figure 2 contains examples from four different AI models, showing that the attack is able to extract nearly-perfect approximations of the original training data from the model updates.</span></span></span></p>
|
||
|
||
<p><span><span><span><strong>How to fix it!</strong></span></span></span></p>
|
||
|
||
<p><span><span><span>Attacks on model updates suggest that federated learning alone is not a complete solution for protecting privacy during the training process. Many defenses against such attacks focus on protecting the model updates during training, so that the organization that aggregates the model updates does not have access to individual updates.</span></span></span></p>
|
||
|
||
<p><span><span><span>Privacy-enhancing technologies that protect the model updates during training are often said to provide <em>input privacy</em> - they prevent the adversary from learning anything about the <em>inputs</em> (i.e. the model updates) to the system. Many approaches for input privacy, including approaches used in the UK-US PETs Prize Challenges, rely on creative applications of cryptography. We’ll highlight several of these solutions throughout this blog series.</span></span></span></p>
|
||
|
||
<p><strong><span><span><span><span>Attacks on Trained Models</span></span></span></span></strong></p>
|
||
|
||
<p><span><span><span>The second major class of attacks target the trained AI model <em>after</em> training has finished. The model is the output of the training process, and often consists of <em>model parameters</em> that control the model’s predictions. This class of attacks attempts to reconstruct the training data <em>from the model’s parameters</em>, without any of the additional information available during the training process. This may sound like a more difficult challenge, but recent research has demonstrated that such attacks are feasible.</span></span></span></p>
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<div data-lightbox="https://www.nist.gov/sites/default/files/styles/2800_x_2800_limit/public/images/2024/01/24/Picture7.png?itok=i3yEoqnh" data-media-id="699136">
|
||
|
||
<img alt="Training data extracted from a trained AI model using the attack developed by Haim et al." height="204" loading="lazy" src="/sites/default/files/styles/480_x_480_limit/public/images/2024/01/24/Picture7.png?itok=u96XoXhW" typeof="foaf:Image" width="480">
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</div>
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<figcaption class="nist-image__caption">Figure 3: Training data extracted from a trained AI model using the attack developed by Haim et al. The top portion of the figure (a) shows extracted data; the bottom portion (b) shows corresponding images from the original training data. Credit: Haim et al.<div class="nist-image__credit">
|
||
<span class="nist-image__credit-label">Credit:</span>
|
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|
||
NIST
|
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|
||
</div></figcaption>
|
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</figure>
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|
||
<p><span><span><span>AI models based on deep learning are particularly susceptible to the extraction of training data from trained models because deep neural networks seem to <em>memorize </em>their training data in many cases. Researchers are still unsure about why this memorization happens, or whether it is strictly necessary to train effective AI models. From a privacy perspective, however, this kind of memorization is a significant problem. <a href="https://arxiv.org/abs/2206.07758">Recent work by Haim et al.</a> demonstrated the feasibility of extracting training data from an AI model trained to recognize objects in images; an example appears in Figure 3.</span></span></span></p>
|
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|
||
<figure class=" nist-image--lightbox nist-image" role="group">
|
||
<div data-lightbox="https://www.nist.gov/sites/default/files/styles/2800_x_2800_limit/public/images/2024/01/24/Picture8.png?itok=yTwGFKZo" data-media-id="699141">
|
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|
||
<img alt="Training data extracted from a diffusion model using the attack developed by Carlini et al." height="176" loading="lazy" src="/sites/default/files/styles/480_x_480_limit/public/images/2024/01/24/Picture8.png?itok=juv0VNwf" typeof="foaf:Image" width="249">
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</div>
|
||
<figcaption class="nist-image__caption">Figure 4: Training data extracted from a diffusion model using the attack developed by Carlini et al. Diffusion models are designed for generating images; one popular example is OpenAI’s DALL-E. Credit: Carlini et al.<div class="nist-image__credit">
|
||
<span class="nist-image__credit-label">Credit:</span>
|
||
|
||
NIST
|
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|
||
</div></figcaption>
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</figure>
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<figure class=" nist-image--lightbox nist-image" role="group">
|
||
<div data-lightbox="https://www.nist.gov/sites/default/files/styles/2800_x_2800_limit/public/images/2024/01/24/Picture9.png?itok=lm58d11_" data-media-id="699146">
|
||
|
||
<img alt="Training data extracted from a large language model (LLM) using the attack developed by Carlini et al. " height="181" loading="lazy" src="/sites/default/files/styles/480_x_480_limit/public/images/2024/01/24/Picture9.png?itok=2kIDjUm9" typeof="foaf:Image" width="181">
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|
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|
||
</div>
|
||
<figcaption class="nist-image__caption">Figure 5: Training data extracted from a large language model (LLM) using the attack developed by Carlini et al. This example is from GPT-2, the predecessor of ChatGPT. Credit: Carlini et al.<div class="nist-image__credit">
|
||
<span class="nist-image__credit-label">Credit:</span>
|
||
|
||
NIST
|
||
|
||
</div></figcaption>
|
||
</figure>
|
||
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|
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|
||
<p><span><span><span>The chal</span></span></span><span><span><span>lenge of memorized training data seems to be even worse for larger, more complex models - including popular large language models (LLMs) like ChatGPT and image generation models like DALL-E. Figure 4 shows an example of extracting training data from an image generation model using <a href="https://arxiv.org/abs/2301.13188">an attack developed by Carlini et al.</a>; Figure 5 shows an example of extracting training data from a large language model using <a href="https://arxiv.org/abs/2012.07805">an attack by Carlini et al.</a></span></span></span></p>
|
||
|
||
<p><span><span><span><strong>How to fix it!</strong></span></span></span></p>
|
||
|
||
<p><span><span><span>Attacks on trained models show that trained models are vulnerable, even when the training process is completely protected. Defenses against such attacks focus on controlling the information content of the trained model itself, to prevent it from revealing too much about the training data.</span></span></span></p>
|
||
|
||
<p><span><span><span>Privacy-enhancing technologies that protect the trained model are often said to provide <em>output privacy</em> - they prevent the adversary from learning anything about the training data from the system’s <em>outputs</em> (i.e. the trained model). The most comprehensive approach for ensuring output privacy is called <em>differential privacy</em>, and is the subject of a previous <a href="https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/focus-areas/de-id/dp-blog">NIST blog series</a> and new <a href="https://csrc.nist.gov/pubs/sp/800/226/ipd">draft guidelines</a>. Many of the solutions developed in the UK-US PETs Prize Challenges leverage differential privacy to defend against attacks on the trained model, which we’ll highlight later in this blog series.</span></span></span></p>
|
||
|
||
<p><span><span><span>As always, we hope to hear from you with any questions and feedback. Please contact us at <span class="spamspan"><span class="u">pets</span> [at] <span class="d">cdei.gov.uk</span><span class="t"> (pets[at]cdei[dot]gov[dot]uk)</span></span> or <span class="spamspan"><span class="u">privacyeng</span> [at] <span class="d">nist.gov</span><span class="t"> (privacyeng[at]nist[dot]gov)</span></span>.</span></span></span></p>
|
||
|
||
<p><strong><span><span><span><span>Coming Up Next</span></span></span></span></strong></p>
|
||
|
||
<p><span><span><span>In our next post, we’ll introduce one of the key issues for federated learning: distribution of the data among the participating entities.</span></span></span></p>
|
||
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||
</div>
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||
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||
<div
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class="nist-block"
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||
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||
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<div class="nist-tags">
|
||
<a href="/cybersecurity-insights-categories/measurement-science" hreflang="en">Measurement Science</a></div>
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||
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||
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||
<article
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||
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|
||
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|
||
<div class="nist-teaser__image">
|
||
<div class="entity-reference"> <a href="/blogs/cybersecurity-insights/authors/joseph-near" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2023/12/06/Jo%20Near.jpg?itok=r50VF0v7" width="86" height="100" alt="Joseph Near" typeof="foaf:Image" />
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</a>
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||
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||
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<h3 class="nist-teaser__title"><a href="/blogs/cybersecurity-insights/authors/joseph-near"><span>Joseph Near</span>
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||
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|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"><p class="MsoBodyText"><span><span><strong>Joseph Near</strong> is an assistant professor of computer science at the University of Vermont who supports NIST as a moderator for the Privacy Engineering Collaboration Space. His research interests include data privacy, computer security, and programming languages. Joseph received his B.S. in computer science from Indiana University, and his M.S. and Ph.D. in computer science from MIT.</span></span></p></div>
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||
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||
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|
||
<div class="nist-teaser__image">
|
||
<div class="entity-reference"> <a href="/blogs/cybersecurity-insights/authors/david-darais" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2023/12/06/darais-new-640%2B240.jpg?itok=ogWja4KM" width="100" height="100" alt="David Darais" typeof="foaf:Image" />
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</div>
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|
||
</a></h3>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"><p><span><span><span><span><span><span><span><span><strong>David Darais</strong> is a Principal Scientist at Galois, Inc. and supports NIST as a moderator for the Privacy Engineering Collaboration Space. David's research focuses on tools for achieving reliable software in critical, security-sensitive, and privacy-sensitive systems. David received his B.S. from the University of Utah, M.S. from Harvard University and Ph.D. from the University of Maryland.</span></span></span></span></span></span></span></span></p></div>
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||
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</article>
|
||
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<div class="nist-field__item">
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|
||
>
|
||
<div class="nist-teaser__image">
|
||
<div class="entity-reference"> <a href="/blogs/cybersecurity-insights/authors/dave-buckley" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2023/12/06/Dave%20Buckley%20UK.jpg?itok=bG0FtUq6" width="88" height="100" alt="Dave Buckley" typeof="foaf:Image" />
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|
||
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|
||
</a></h3>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"><p><span><span><span>Dave Buckley is a Senior Technology Adviser at the UK’s Centre for Data Ethics and Innovation, where he leads the Centre’s work on privacy enhancing technologies. He has led development of CDEI’s PETs Adoption Guide, was a co-editor of the 2023 UN PET Guide, and was a member of the core delivery team for the UK-US PETs prize challenges. Dave is a software engineer and data scientist by background, with a strong interest in quantitative methods for social science.</span></span></span></p></div>
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||
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<div class="nist-teaser__image">
|
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<div class="entity-reference"> <a href="/blogs/cybersecurity-insights/authors/mark-durkee" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2024/01/24/Mark%20Durkee%20.png?itok=co4ZojG9" width="75" height="100" alt="Mark Durkee " typeof="foaf:Image" />
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|
||
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|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"><p><span><span><em><span><span>Mark Durkee is Head of Data & Technology at the Centre for Data Ethics and Innovation (CDEI). He leads a portfolio of work including the CDEI's work on the Algorithmic Transparency Recording Standard, privacy enhancing technologies, and a broader programme of work focused on promoting responsible access to data. He previously led CDEI's independent review into bias in algorithmic decision-making. He has spent over a decade working in a variety of technology strategy, architecture and cyber security roles within the UK government, and previously worked as a software engineer and completed a PhD in Theoretical Physics.</span></span></em></span></span></p></div>
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|
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>Related posts</h2>
|
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<div><div class="js-view-dom-id-b56fda91bd968659a766751cabb7418b01f21bbf4e458ba387432e36cd58cf01">
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<h3 class="nist-teaser__title"><a href="/blogs/cybersecurity-insights/how-will-they-measure-rti-awarded-evaluate-class-2016-state-and-local"><span>How will they measure up? RTI awarded to evaluate the class of 2016 state and local pilots!</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__date">
|
||
<div class="daterange"><time datetime="2017-09-07T12:00:00Z">September 7, 2017</time>
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<div class="nist-teaser__content">
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<div class="text-with-summary"> NIST is the federal government’s measurement agency. While the ultimate goal is innovation and adoption in the market, we need to know that the solutions being
|
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|
||
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|
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<div class="nist-teaser__content-wrapper">
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<header>
|
||
<h3 class="nist-teaser__title"><a href="/blogs/cybersecurity-insights/2016-year-review-tig-ing-stock-innovation-identity-ecosystem"><span>2016 Year in Review: (TIG-ing stock of) Innovation in the Identity Ecosystem</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__date">
|
||
<div class="daterange"><time datetime="2016-12-30T12:00:00Z">December 30, 2016</time>
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</header>
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||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> When you think about 2016, the first thing that comes to mind is innovation in the identity ecosystem. That can’t just be us, right? While there has been a host
|
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