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  • PhD Thesis Defense - Brendan Avent

    Fri, Apr 21, 2023 @ 04:00 PM - 06:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Thesis Defense - Brendan Avent

    Title: Practice-Inspired Models and Mechanisms for Differential Privacy

    Committee Members: Aleksandra Korolova (chair), Salman Avestimehr (Department of Electrical and Computer Engineering), Leana Golubchik, David Kempe, Cyrus Shahabi

    Abstract: Now more than ever, organizations such as companies, governments, and researchers must collect and analyze people's sensitive data to drive decisions and fuel innovation. Differential privacy has become the gold standard for data privacy in computer science literature, particularly for privacy-preserving data analysis and machine learning. Significant research effort has been devoted to designing and theoretically analyzing mechanisms that satisfy differential privacy. However, far less research has studied the pragmatic considerations of differential privacy, i.e., how its trust models and mechanisms can be adapted and applied for real-world uses.

    I focus on making differential privacy useful for real-world applications by removing barriers that hinder its adoption in practice. In the first part, I address the utility gap between the more and less desirable trust models of differential privacy by defining and analyzing a new hybrid trust model. In the second part, I address the lack of tools for analyzing the utility of complex differentially private mechanisms by developing a new method for quantifying such mechanisms privacy--utility trade-offs. Finally, I show how to improve the utility of DP mechanisms that answer statistical queries on a large scale. In the classic setting where all queries are provided to the mechanism in advance, I detail how we extend the state-of-the-art differentially private mechanism for answering marginal queries to a more general, flexible query class. I then define a new setting where our extended mechanism is only provided partial knowledge of which queries will be posed. Analyzing the mechanism in both settings, I show that it answers a massive number of queries both efficiently and effectively.

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/95544425859?pwd=Wk82dTBEQkhyMDFxeGtqS2VqK0h5UT09 ;Meeting ID: 955 4442 5859 ;Passcode: 645176

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