Sajin Sasy is a PhD candidate in the Cryptography, Security, and Privacy (CrySP) group at the University of Waterloo, advised by Ian Goldberg. His work focuses on improving the security and privacy of individuals' data and communications online, through research spanning the fields of cryptography, design and analysis of algorithms, distributed systems, and machine learning. In particular, his work presents novel privacy-preserving computation protocols that improve asymptotics, underlying constants, wall-clock time, and parallelizability over state-of-the-art solutions. His works have been published in top-tier systems security venues (ACM CCS and NDSS), privacy venues (PoPETs), and in AI and machine learning venues (NeurIPS and AAAI). His research is supported by an NSERC Collaborative Research and Development Grant with the Royal Bank of Canada.

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AG Audience

English

60 Minutes

Bochum

People around the world use data-driven online services every day. However, data center attacks and data breaches have become a regular and rising phenomenon. How, then, can one reap the benefits of data-driven statistical insights without compromising the privacy of individuals' data? In this talk, I will first present an overview of three disparate approaches towards privacy-preserving computations today, namely homomorphic cryptography, distributed trust, and secure hardware. These ostensibly unconnected approaches have one unifying element: oblivious algorithms. I will discuss the relevance and pervasiveness of oblivious algorithms in all the different models for privacy-preserving computations. Finally, I highlight the performance and security challenges in deploying such privacy-preserving solutions, and present three of my works that mitigate these obstacles through the design of novel efficient oblivious algorithms.

Lena Schneider

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