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<div class="" style="word-wrap:break-word"><span class="" style="font-size:14.666666984558105px">This is an announcement of Emily Wenger's Candidacy Exam.</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">Candidate: Emily Wenger</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Date: Friday, June 17, 2022</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Time: 10:30 am 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!8f6DRXHMJMMsPbSk3c0ycCPF0YzX-n468F7PoRjjtu_-HFNo1reoIwGgwptwnw2M-ELs_J8d6DB2Ujt5XGroHiB5dbO_RUMv7Bfe$" 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: Towards 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 expanded in size and scope in recent years, so has the amount of data needed to train them. This creates privacy risks for individuals whose data -- be it their
images, emails, tweets, or browsing history -- is used for training. For example, ML models can memorize their training data, revealing private information about individuals in the dataset. Furthermore, users whose data is co-opted for ML use may end up enrolled
in a privacy-compromising system, such as a large-scale facial recognition model. Most existing work on ML data privacy accepts the premise that data use is inevitable and instead tries to mitigate privacy risks during model training. However, privacy-conscious
individuals may desire agency over how and if their data is used, rather than only having their privacy preserved when it is used. 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
privacy protection approaches, and it is the focus of this thesis. </span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Data agency can take many forms, and this thesis will develop technical solutions that enable individuals to disrupt or discover when their data is used in large-scale ML systems. It targets data agency
in the context of large-scale facial recognition (FR) systems, providing ways for users to combat unwanted facial recognition. This work proposes three data agency solutions to disrupt or trace data use in FR systems or, in extreme cases, directly attack the
FR system. Additionally, it develops a framework for reasoning about broadly about data agency in the context of FR. It will use this framework to outline both technical and social challenges of data agency solutions in the FR space and propose directions
for future research. Finally, this thesis will discuss the connections between the proposed FR-specific data agency solutions and methods for reclaiming data agency in other domains.</span><br class="" style="font-size:14.666666984558105px">
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<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><br class="" style="font-size:14.666666984558105px">
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