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<h1 class="nist-page__title">Protecting Model Updates in Privacy-Preserving Federated Learning: Part Two</h1>
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<div class="daterange"><time datetime="2024-05-02T12:00:00Z">May 2, 2024</time>
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<span>By: </span>
<a href="/blogs/cybersecurity-insights/authors/david-darais" hreflang="en">David Darais</a>, <a href="/blogs/cybersecurity-insights/authors/joseph-near" hreflang="en">Joseph Near</a>, <a href="/blogs/cybersecurity-insights/authors/mark-durkee" hreflang="en">Mark Durkee</a> and <a href="/blogs/cybersecurity-insights/authors/dave-buckley" hreflang="en">Dave Buckley</a>
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<p><span lang="EN"><strong>The problem&nbsp;</strong></span></p><p class="MsoNormal"><span>The&nbsp;</span><a href="https://www.nist.gov/blogs/cybersecurity-insights/protecting-model-updates-privacy-preserving-federated-learning"><span lang="EN-US">previous post</span></a><span>&nbsp;in our series discussed techniques for providing input privacy in PPFL systems where data is horizontally partitioned. This blog will focus on techniques for providing input privacy when data is </span><em><span><strong>vertically partitioned</strong></span></em><span>.</span><span lang="EN"></span></p><p class="MsoNormal"><span lang="EN">&nbsp;</span><span>As described in our&nbsp;</span><a href="https://www.nist.gov/blogs/cybersecurity-insights/data-distribution-privacy-preserving-federated-learning"><span lang="EN-US">third post</span></a><span>, vertical partitioning is where the training data is divided across parties such that each party holds different columns of the data. In contrast to horizontally partitioned data, training a model on vertically partitioned data is more challenging as it is generally not possible to train separate models on different </span><em><span>columns</span></em><span> of the data (but overlapping </span><em><span>rows</span></em><span>) and then compose them afterwards.</span><span lang="EN"></span><span lang="EN"></span></p><p class="MsoNormal"><span>Instead, we need methods to be able to train on the collective data while protecting the privacy of that data. One critical step required for this is </span><em><span><strong>privacy-preserving entity alignment:&nbsp;</strong></span></em><span>matching corresponding records (e.g. records referencing the same person) across different datasets</span><em><span><strong>.&nbsp;</strong></span></em><span>Parties can use the result of the entity alignment to train a model in a similar way to how they would in a horizontal partitioning scenario.&nbsp;</span></p><p class="MsoNormal"><span>Matching corresponding records without revealing the records themselves is a challenging task. In the remainder of this blog, we discuss two methods for privacy-preserving entity alignment: private set intersection and Bloom filters.</span></p><p><span lang="EN"><strong>Private set intersection</strong></span></p><p class="MsoNormal"><span>A </span><em><span><strong>private set intersection (PSI)&nbsp;</strong></span></em><span>is an input privacy technique that enables data linking between parties. The result of a PSI process reveals information to the participants </span><em><span>only</span></em><span> for rows of data that match on a common key. This is illustrated in Figure 1. For example,&nbsp;</span><a href="https://mccourt.georgetown.edu/news/a-federal-government-privacy-preserving-technology-demonstration/"><span lang="EN-US">a pilot research project led by researchers at Georgetown University</span></a><span> applied PSI to link U.S. Department of Education data with data from the National Student Loan Data System to compute financial aid statistics without revealing students’ social security numbers.</span></p>
<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/2024/05/02/Naomi%20Privacy%20Blog%205_1.png?itok=eXZWjCsp" data-media-id="707161">
<img alt="The result of carrying out a private set intersection between two data partitions that share a common id field." height="195" loading="lazy" src="/sites/default/files/styles/480_x_480_limit/public/images/2024/05/02/Naomi%20Privacy%20Blog%205_1.png?itok=61sdlxlU" typeof="foaf:Image" width="388">
</div>
<figcaption class="nist-image__caption">The result of carrying out a private set intersection between two data partitions that share a common id field.<div class="nist-image__credit">
<span class="nist-image__credit-label">Credit:</span>
NIST
</div></figcaption>
</figure>
<p class="MsoNormal"><span>Some PSI approaches reveal only the </span><em><span>number</span></em><span> of matching rows, while others reveal more information (e.g. the </span><em><span>contents</span></em><span> of matching rows, as in the example in Figure 1). PPFL systems often rely on the contents of the matching rows, and must therefore use “leaky” PSI approaches. There are also some trade-offs to consider between the strength of a PSI technique in protecting data against its performance when applied in federated learning scenarios on large data sets such as those in the UK-US PETs Prize Challenges.</span></p><p><span lang="EN"><strong>Bloom filters</strong></span></p><p class="MsoNormal"><span>A second approach to entity alignment is to construct a&nbsp;</span><a href="https://dl.acm.org/doi/abs/10.1145/362686.362692"><em><span lang="EN-US"><strong>Bloom filter</strong></span></em></a><span>---a probabilistic data structure that uses a collection of hash functions to enable efficient storage and lookup of elements. Since Bloom filters are probabilistic, they sometimes make mistakes (false positives).</span></p><p class="MsoNormal"><span lang="EN-GB"></span></p><p class="MsoNormal"><span lang="EN-GB">In the context of PPFL, Bloom filters can </span><em><span lang="EN-GB">also</span></em><span lang="EN-GB"> provide privacy benefits. Specifically, the false positives provide a kind of input privacy protection automatically. Some PPFL systems use Bloom filters to facilitate privacy-preserving </span><em><span lang="EN-GB">feature mining</span></em><span lang="EN-GB">—a process that determines which attributes of the data to use when building the model. This was the approach used by&nbsp;</span><a href="https://github.com/usnistgov/PrivacyEngCollabSpace/tree/master/tools/de-identification/Scarlet-PETs"><span lang="EN-GB">Scarlet-PETs</span></a><span lang="EN-GB">, a winning team from the US side of the&nbsp;</span><a href="https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/challenges/prize-challenges"><span lang="EN-GB">UK-US PETs Prize Challenges</span></a><span lang="EN-GB">. Their solution is shown in Figure 2.</span></p>
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<div data-lightbox="https://www.nist.gov/sites/default/files/styles/2800_x_2800_limit/public/images/2024/05/02/Naomi%20Privacy%20Blog%205_2.png?itok=OGGkT1sq" data-media-id="707166">
<img alt="Architecture of Scarlet-PETs solution for the financial crime track of the UK-US PETs prize challenges, leveraging Bloom filters for privacy-preserving feature mining." height="125" loading="lazy" src="/sites/default/files/styles/480_x_480_limit/public/images/2024/05/02/Naomi%20Privacy%20Blog%205_2.png?itok=oWNhvV_d" typeof="foaf:Image" width="480">
</div>
<figcaption class="nist-image__caption">Architecture of Scarlet-PETs solution for the financial crime track of the UK-US PETs prize challenges, leveraging Bloom filters for privacy-preserving feature mining.<div class="nist-image__credit">
<span class="nist-image__credit-label">Credit:</span>
NIST
</div></figcaption>
</figure>
<p class="MsoNormal"><span lang="EN-GB">In the first step, feature mining is carried out locally at each bank to create local Bloom filters containing accounts that are deemed potentially suspicious based on the account-level data. These local filters are aggregated into a global Bloom filter, which is used to augment the payment network transaction data. Since Bloom filters are represented as strings of bits, they can be aggregated using the techniques described in&nbsp;</span><a href="https://www.nist.gov/blogs/cybersecurity-insights/protecting-model-updates-privacy-preserving-federated-learning"><span lang="EN-GB">our fourth post</span></a><span lang="EN-GB">. Finally, the payment network adds an additional binary field “BF” to the transaction data, which is 1 if either the sending or receiving account for the transaction is present in the global Bloom filter, and 0 otherwise. This completes the entity alignment step, and a (differentially private) classifier can then be trained on this augmented data.</span></p><p class="MsoNormal"><span lang="EN-GB"></span></p><p class="MsoNormal"><span lang="EN">The use of Bloom filters helps protect input privacy as there is no direct data sharing between the different parties; only information regarding the potential presence or absence of a specific feature is revealed, not the actual content of the data itself. However, it may still be possible for sensitive data to be leaked via a Bloom filter. In the example above, the global Bloom filter is shared with the&nbsp;</span><span lang="EN-GB">payment network</span><span lang="EN">. Depending on the accuracy of the filter and how it has been constructed, it may be possible for the&nbsp;</span><span lang="EN-GB">payment network </span><span lang="EN">to learn some information about the bank data that they should not. At minimum, in this setup the&nbsp;</span><span lang="EN-GB">payment network&nbsp;</span><span lang="EN">is able to learn that for transactions where BF = 1 there is a high probability that one of the banks has flagged either the sending or receiving account (or both) as suspicious. In the real world, a policy decision would need to be made between the banks and the&nbsp;</span><span lang="EN-GB">payment network&nbsp;</span><span lang="EN">to determine whether such data leakage would be permissible.</span></p><p><span lang="EN"><strong>Balancing performance with leakage</strong></span></p><p class="MsoNormal"><span>The techniques described in this post provide efficient methods for performing entity alignment with input privacy in PPFL, but they both leak some information about the private data involved. In the case of PSI, the system may leak information about the number or even the contents of matching rows; in the case of Bloom filters, the system will leak (noisy) information about the matching status of each row. It’s possible to layer additional techniques on top to eliminate this leakage such as secure multiparty computation, fully-homomorphic encryption, or secure enclaves---but as discussed&nbsp;</span><a href="https://www.nist.gov/blogs/cybersecurity-insights/protecting-model-updates-privacy-preserving-federated-learning"><span lang="EN-US">in our fourth post</span></a><span>, these techniques often carry a significant performance cost.</span></p><p class="MsoNormal"><span lang="EN"></span></p><p class="MsoNormal"><span>System designers must carefully consider the leakage of these techniques as part of the system’s </span><em><span>threat model</span></em><span>—the description of attackers the system is supposed to defend against. In some cases, the additional leakage poses little privacy risk, and it may be acceptable to use PSI or a Bloom filter as part of the system. In other cases, the leakage poses major privacy risks, and requires the use of additional techniques. Balancing the privacy risk of information leakage against the performance cost of preventing those leaks remains a significant challenge in vertical federated learning.</span></p><p><span lang="EN"><strong>Coming up next</strong><
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