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<div class="" style="word-wrap:break-word; line-break:after-white-space"><span class="" style="font-size:14.666666984558105px">This is an announcement of Emily Wenger's Dissertation Defense.</span><br class="">
<span class="" style="font-size:14.666666984558105px">===============================================</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Candidate: Emily Wenger</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Date: Monday, April 10, 2023</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Time: 1:30 pm CST</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Remote Location: </span><a href="https://urldefense.com/v3/__https://uchicago.zoom.us/j/4571855061?pwd=d1BURDhucEVOR1JjaVY2V2cvRENWZz09__;!!BpyFHLRN4TMTrA!5jCQUIxvKncWYEG4vQX-FIjkG5GyFiBoIZsqq4XxBtxNtNiC2bn4ustaIMjMab5pcPrJLI0HDfH6oWcX-e3iGoJ9EUY0DiBe4sGQ$" class="" style="font-size:14.666666984558105px">https://uchicago.zoom.us/j/4571855061?pwd=d1BURDhucEVOR1JjaVY2V2cvRENWZz09</a><span class="" style="font-size:14.666666984558105px">
Meeting ID: 457 185 5061 Passcode: 645188</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Location: JCL 298</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Title: Reclaiming Data Agency in the Age of Ubiquitous Machine Learning</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Abstract: As machine learning (ML) models have grown in size and scope in recent years, so has the amount of data needed to train them. Unfortunately, individuals whose data is used in large-scale ML models
may face unwanted consequences. Such data use may violate individuals' privacy or enroll them in an unwanted ML application. Furthermore, recent advances have greatly enhanced models' ability to generate synthetic data like text and images. This has unleashed
a fresh wave of privacy and intellectual property concerns, as generative models can memorize and regurgitate their training data, and are trained on massive datasets scraped from the internet.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px"> </span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">While user data privacy issues are well-recognized in the ML research community, most attempts to address it take a model-centric approach. Existing solutions assume that model trainers are well-intended
and that data has been taken with consent, or that data use is inevitable and that the best path forward is to mitigate privacy risks.These solutions work but overlook a significant problem: often data is taken without consent, and users do not trust model
trainers.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px"> </span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">This begs the question: what if data use was not inevitable? What if, instead, users had agency over how and if their data is used in ML systems? This thesis argues that data agency, the ability to know
and control how and if one's data is used in ML systems, is an important complement to existing ML data privacy protection approaches. Such agency would shift the current power dynamic, which renders users helpless at the hands of model creators, and help
users control their digital destinies. Solutions of this nature would accentuate current work on data privacy, giving users, not just model trainers, control over how their data is used. </span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px"> </span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">This thesis explores solutions that provide users with data agency against large-scale ML systems, allowing individuals to disrupt or discover when their data is used in large-scale ML systems. It proposes
three solutions that prevent or trace data use in ML systems or, in extreme cases, directly attack the ML system. It focuses on the use case of large-scale facial recognition (FR) systems, a machine learning technology that has recently become a flashpoint
for civil liberties and privacy issues. With this use case in mind, the thesis finally develops a framework for reasoning about broadly about FR data agency.</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Advisors: Ben Zhao and Heather Zheng</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Committee Members: Ben Zhao, Heather Zheng, Yuxin Chen, and Aloni Cohen</span>
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