(The talk will be given in English)
Speaker: Dr. Eliad Tsfadia
Department of Computer Science at Georgetown University
|
011 hall, Electrical Engineering-Kitot Building
|
Sunday, December 22nd, 2024
15:00 - 16:00
Can We Bypass the Curse of Dimensionality in Private Data Analysis?
Abstract
Differentially private (DP) algorithms typically exhibit a significant dependence on the dimensionality of their input, as their error or sample complexity tends to grow polynomially with the dimension. This cost of dimensionality is inherent in many problems, as Bun, Ullman, and Vadhan (STOC 2014) showed that any method that achieves lower error rates is vulnerable to tracing attacks (also known as membership inference attacks). Unfortunately, such costs are often too high in many real-world scenarios, such as training large neural networks, where the number of parameters (the ambient dimension) is very high.
On the positive side, the lower bounds do not rule out the possibility of reducing error rates for "easy" inputs. But what constitutes "easy" inputs? And how likely is it to encounter such inputs in real-world scenarios?
In this talk, I will present a few ways to quantify "input easiness" for the fundamental task of private averaging and support them with upper and lower bounds. In particular, I will show types of properties that are both sufficient and necessary for eliminating the polynomial dependency on the dimension.
I will conclude by outlining future research directions and providing a broader perspective on my work.
The talk is mainly based on the following three papers:
Short Bio
Eliad Tsfadia is a postdoctoral researcher in the Department of Computer Science at Georgetown University, hosted by Prof. Kobbi Nissim. He completed his Ph.D. in the Department of Computer Science at Tel Aviv University under the supervision of Prof. Iftach Haitner. In addition, Eliad was a research intern at Google Research - Israel (2019-2022), a part-time security researcher at IBM Research - Haifa (2017-2019), and a full-time software engineer, team leader, and officer (Major) in the technological unit of the Intelligence Corps at IDF (2008-2017). Eliad's primary research interests lie in Data Privacy, particularly at the intersections with Cryptography and Machine Learning.
השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז. בטופס הנוכחות שיועבר באולם במהלך הסמינר