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<!doctype html>
<html>
<!-- HEAD -->
<head>
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-113874144-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
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<title>Clement Fung's Page</title>
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<div class="container">
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<div class="profile-usertitle">
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Clement Fung
</div>
<div class="profile-usertitle-job">
PhD Student <br>
Carnegie Mellon University
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Home </a>
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Research </a>
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CV </a>
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Google Scholar </a>
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Misc </a>
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<div class="col-md-9">
<div class="profile-content">
<h3> Selected Publications </h3>
<hr border-color="black">
<br>
<p>
<b>Perspectives from a Comprehensive Evaluation of Reconstruction-based Anomaly Detection in Industrial Control Systems</b> <br>
<u>Clement Fung</u>, Shreya Srinarasi, Keane Lucas, Hay Bryan Phee, Lujo Bauer.<br>
<i><a href="https://esorics2022.compute.dtu.dk/">27th European Symposium on Research in Computer Security (ESORICS 2022)</a></i><br>
Copenhagen, Denmark. September 2022. <br>
[<a href="gallery/papers/esorics2022-ics-anomaly-detection.pdf">PDF</a>]
[<a href="https://link.springer.com/chapter/10.1007/978-3-031-17143-7_24">Springer</a>]
[<a href="https://www.youtube.com/watch?v=vHbY7HsBUKQ">Video</a>]
[<a href="gallery/slides/esorics22-slides.pdf">Slides</a>]
[<a href="https://github.com/pwwl/ics-anomaly-detection">Code</a>]
</p>
<br>
<p>
<b>Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning</b> <br>
Muhammad Shayan, <u>Clement Fung</u>, Chris J.M. Yoon, Ivan Beschastnikh.<br>
<i><a href="https://www.computer.org/csdl/journal/td">IEEE Transactions on Parallel and Distributed Systems (TPDS)</a></i><br>
Volume 32, Issue 7. July 2021. <br>
[<a href="gallery/papers/tpds2020-biscotti-final.pdf">PDF</a>]
[<a href="https://ieeexplore.ieee.org/document/9292450">IEEE</a>]
[<a href="https://github.com/DistributedML/Biscotti">Code</a>]
</p>
<ul>
<li><p>
A full version of this paper is <a href="https://arxiv.org/abs/1811.09904">available on arXiv</a>.
</p></li>
</ul>
<br>
<p>
<b>Towards a Lightweight, Hybrid Approach for Detecting DOM XSS Vulnerabilities with Machine Learning</b>
<br>
William Melicher, <u>Clement Fung</u>, Lujo Bauer, Limin Jia.<br>
<i><a href="https://www2021.thewebconf.org/">The Web Conference 2021</a></i><br>
Ljubjana, Slovenia (Virtual). April 2021. <br>
[<a href="gallery/papers/www2021-camera.pdf">PDF</a>]
[<a href="https://www.youtube.com/watch?v=RaEGCln9mg0">Video</a>]
[<a href="https://github.com/pwwl/www-dom-xss-tools">Code</a>]
</p>
<br>
<p>
<b>The Limitations of Federated Learning in Sybil Settings</b> <br>
<u>Clement Fung</u>, Chris J.M. Yoon, Ivan Beschastnikh.<br>
<i><a href="https://raid2020.org/">23rd International Symposium on Research in Attacks, Intrusions and Defenses
(RAID 2020)</a></i><br>
Donostia/San Sebastian, Spain (Virtual). October 2020.<br>
[<a href="gallery/papers/raid20-final.pdf">PDF</a>]
[<a href="gallery/slides/raid2020-FoolsGold.pdf">Slides</a>]
[<a href="https://www.youtube.com/watch?v=NSuFm97ipX0">Video</a>]
[<a href="https://github.com/DistributedML/FoolsGold">Code</a>]
</p>
<ul>
<li>
A longer paper describing the FoolsGold algorithm is <a href="https://arxiv.org/pdf/1808.04866.pdf">available on arXiv</a>.
</li>
<li>
<p>This work was also featured on an episode of the <a href="https://podcasts.apple.com/us/podcast/sybil-attacks-on-federated-learning/id890348705?i=1000498506776">Data Skeptic Podcast</a>!
</p>
</li>
</ul>
<br>
<p>
<b>Brokered Agreements in Multi-Party Machine Learning</b> <br>
<u>Clement Fung</u>, Ivan Beschastnikh.<br>
<i><a href="https://icsr.zju.edu.cn/apsys2019/">
10th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys 2019)</a></i><br>
Hangzhou, China. August 2019.<br>
[<a href="gallery/papers/apsys19-final.pdf">PDF</a>]
[<a href="https://dl.acm.org/citation.cfm?id=3343744">ACM</a>]
[<a href="gallery/slides/ApSys2019-08-19-2019-Final.pdf">Slides</a>]
[<a href="https://github.com/DistributedML/TorML">Code</a>]
</p>
<ul>
<li><p>
A longer paper describing the TorMentor system is <a href="https://arxiv.org/pdf/1811.09712.pdf">available on arXiv</a>.</p>
</li>
</ul>
<br>
<p>
<b>GainForest: Scaling Climate Finance for Forest Conservation using
Interpretable Machine Learning on Satellite Imagery</b> <br>
David Dao, Catherine Cang, <u>Clement Fung</u>, Ming Zhang, Nick Pawlowski, Reuven Gonzales, Nick Beglinger, Ce Zhang.<br>
<i><a href="https://www.climatechange.ai/ICML2019_workshop.html">
Climate Change: How Can AI Help?: ICML 2019 Workshop</a></i><br>
Long Beach, CA. June 2019. <br>
[<a href="gallery/papers/icml2019-gainforest.pdf">PDF</a>]
[<a href="gallery/posters/gainforest-icml19.pdf">Poster</a>]
</p>
<!--
<h4> Technical Reports </h4>
<ul id="pubs">
</ul>
-->
<h3> Posters </h3>
<hr border-color="black">
<br>
<p>
<b>Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning </b><br>
Muhammad Shayan, <u>Clement Fung</u>, Chris J.M. Yoon, Ivan Beschastnikh.<br>
<i><a href="https://www.usenix.org/conference/nsdi19/poster-session">NSDI 2019 Poster Session</a></i><br>
Boston, MA. February 2019.<br>
[<a href="gallery/posters/biscotti-nsdi19.pdf">PDF</a>]
</p>
<h3> Other External Talks </h3>
<hr border-color="black">
<br>
<p>
<b> Detecting and Explaining Anomalies in Industrial Control </b> <br>
<i><a href="https://www.cylab.cmu.edu/events/partners_conference/2022/index.html">2022 CyLab Partners Conference</a></i>, Pittsburgh, PA, USA. October 2022.<br>
</p>
<br>
<p>
<b> Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting </b> <br>
<i><a href="http://blogs.ubc.ca/cybersecuritysummit/">UBC Cybersecurity Summit</a></i>, Vancouver, BC, Canada. May 2018.<br>
[<a href="https://youtu.be/rV6U-jy8e1k?t=1h13m30s">Video</a>] [<a href="gallery/posters/tormentor-ubc18.pdf">Poster</a>]
</p>
<br>
<p>
<b> Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting </b> <br>
University of Toronto, Toronto, ON, Canada. December 2017.<br>
[<a href="http://www.csl.utoronto.ca/2017/12/15/dancing-in-the-dark-private-multi-party-machine-learning-in-an-untrusted-setting/">Link</a>]
</p>
</div>
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