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<h1 class="post-title">
<span class="font-weight-bold">Shubham</span> Singh
</h1>
<p class="desc">(he/him) CS Ph.D. Student at UIC.</p>
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<p>1300 SEO</p> <p>851 S. Morgan St., </p> <p>Chicago, Illinois 60607</p>
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<p>I am a Ph.D. student with the Department of Computer Science at the <a href="https://cs.uic.edu/" target="\_blank">University of Illinois at Chicago</a>. I’m advised by <a href="https://www.cs.uic.edu/~ckanich/" target="\_blank">Chris Kanich</a>, and my research focuses on the areas of Security and Privacy, Algorithmic Fairness, and Computational Social Science.</p>
<p>In the past, I worked at <a href="http://precog.iiitd.edu.in/" target="\_blank">Precog Research Lab</a> as a Research Associate, where I was advised by <a href="https://twitter.com/ponguru" target="\_blank">Ponnurangam Kumaraguru “PK”</a> and <a href="http://faculty.iiitd.ac.in/~arunb/" target="\_blank">Arun Balaji Buduru</a>. My industrial experience includes working as a Full Stack Developer at an <a href="https://www.linkedin.com/company/tapzo/">Amazon Pay-acquired startup</a> and a software developer intern at <a href="https://www.mobikwik.com/" target="\_blank">MobiKwik</a>. I received my undergraduate degree from <a href="https://www.iiitd.ac.in/" target="\_blank">IIIT-Delhi</a> in Computer Science.</p>
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<h2>news</h2>
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<th scope="row">Apr 23, 2021</th>
<td>
I passed my Ph.D. qualifier exam! As a part of the written critique and
presentation, I wrote a report titled <a href="assets/pdf/wcp_blackbox_fairness_report.pdf" target="\_blank">“Fighting the Fair Fight: An Overview of the Black-Box Fairness Auditing Techniques”</a>.
</td>
</tr>
<tr>
<th scope="row">Jan 1, 2021</th>
<td>
I am one of the Graduate Teaching Assistants for the Spring 2021 offering
of <a href="https://www.cs.uic.edu/~ckanich/cs361/s21/" target="\_blank">CS 361: Computer Systems Programming</a>.
</td>
</tr>
<tr>
<th scope="row">Dec 7, 2020</th>
<td>
Our paper titled <a href="assets/pdf/alethia_usenix_2021.pdf" target="\_blank">“Helping Users Automatically Find and Manage Sensitive,
Expendable Files in Cloud
Storage”</a> has been
accepted at the 30th USENIX Security Symposium. The camera-ready version of the
paper is available at the link.
</td>
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<div class="publications">
<h2>selected publications</h2>
<ol class="bibliography"><li><div class="row">
<div class="col-sm-2 abbr">
<abbr class="badge">USENIX</abbr>
</div>
<div id="taha_alethia_2021" class="col-sm-8">
<div class="title">Helping Users Automatically Find and Manage Sensitive, Expendable Files in Cloud Storage</div>
<div class="author">
Khan, Mohammad Taha,
Tran, Christopher,
<em>Singh, Shubham</em>,
Vasilkov, Dimitri,
Kanich, Chris,
Ur, Blase,
and Zheleva, Elena
</div>
<div class="periodical">
<em>In 30th USENIX Security Symposium (USENIX Security 21)</em>
2021
</div>
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<a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
<a href="https://www.usenix.org/conference/usenixsecurity21/presentation/khan-mohammad" class="btn btn-sm z-depth-0" role="button" target="_blank">HTML</a>
<a href="/assets/pdf/alethia_usenix_2021.pdf" class="btn btn-sm z-depth-0" role="button" target="_blank">PDF</a>
</div>
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<div class="abstract hidden">
<p>With the ubiquity of data breaches, forgotten-about files stored in the cloud create latent privacy risks. We take a holistic approach to help users identify sensitive, unwanted files in cloud storage. We first conducted 17 qualitative interviews to characterize factors that make humans perceive a file as sensitive, useful, and worthy of either protection or deletion. Building on our findings, we conducted a primarily quantitative online study. We showed 108 long-term users of Google Drive or Dropbox a selection of files from their accounts. They labeled and explained these files’ sensitivity, usefulness, and desired management (whether they wanted to keep, delete, or protect them). For each file, we collected many metadata and content features, building a training dataset of 3,525 labeled files. We then built Aletheia, which predicts a file’s perceived sensitivity and usefulness, as well as its desired management. Aletheia improves over state-of-the-art baselines by 26% to 159%, predicting users’ desired file-management decisions with 79% accuracy. Notably, predicting subjective perceptions of usefulness and sensitivity led to a 10% absolute accuracy improvement in predicting desired file-management decisions. Aletheia’s performance validates a human-centric approach to feature selection when using inference techniques on subjective security-related tasks. It also improves upon the state of the art in minimizing the attack surface of cloud accounts.</p>
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<div class="contact-note">You can reach out to me via email. Also feel free to connect with me on any of the socials.
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