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Differential Privacy for Non-standard Settings
May 28 @ 11:00 am - 12:00 pm PDT
Name: Joann Chen
Chair: Prof. Zhou Li
Date: May 28, 2024
Time: 11:00 AM
Location: EH 5204
Committee: Prof. Yanning Shen, Prof. Gene Tsudik
Title: Differential Privacy for Non-standard Settings
Abstract: In today’s technologically driven world, personal data stands at a crucial juncture, representing both immense potential and significant peril. Such potential, when leveraged properly, can lead to groundbreaking advancements in sectors such as healthcare and finance. Yet, the misuse of this same data can result in severe privacy breaches. This thesis is dedicated to navigating the complex terrain of personal data privacy, highlighting Differential Privacy (DP) as a pivotal solution that provides strong, provable privacy guarantees.
With a focus on DP-aware system design, this thesis delves into the challenges of integrating DP into various systems, emphasizing the necessity for infrastructures that prioritize privacy preservation. It outlines the progression of DP from theoretical concepts to practical implementations, detailing both the challenges encountered and the progress made in applying academic DP concepts to real-world solutions. Moreover, this thesis extends beyond the scope of DP, exploring the broader realm of privacy-enhancing technologies (PETs). These technologies are crucial not only for meeting legal compliance and ensuring privacy in systems and machine learning but also for paving the way towards a future where data utility and individual privacy coexist in harmony.
Short Bio: Joann is a fifth-year Ph.D. candidate in the EECS department advised by Prof. Zhou Li. Her research interests center around Differential Privacy (DP), privacy-enhancing technologies, and privacy in machine learning. She has experience in quantifying privacy risks in machine learning and building DP into DNS resolution, data stream release, resource allocator, ad conversion measurements, and network data synthesis. Her research aims to bridge the gap between DP theory and its practical applications for real-world deployments.