세미나

[세미나] Optimizing Homomorphic Evaluation Curcuits by Program Synthesis and Term Rewriting - 이우석 교수 (한양대학교)
작성일2020-11-27

다음주 저희 정보보호대학원에서는 한양대 이우석 교수님을 모시고

"Optimizing Homomorphic Evaluation Curcuits by Program Synthesis and Term Rewriting" 주제로 아래와 같이 세미나를 개최하고자 합니다.

코로나19 확산방지를 위하여 원격수업으로(ZOOM) 진행할 예정입니다.

 

= 아 래 =

o 일시

20.12.01(화) 16:00~

※ 시작시간 5분전에 준비하여 주세요.

 

URLhttps://zoom.us/j/2902905410

접속 비밀번호: 이메일 별도 공지

 

 

==============================     

Title: Optimizing homomorphic evaluation circuits by program synthesis and term rewriting

 

 

Abstract: 

We present a new and general method for optimizing homomorphic evaluation circuits. Although fully homomorphic encryption (FHE) holds the promise of enabling safe and secure third-party computation, building FHE applications has been challenging due to their high computational costs. Domain-specific optimizations require a great deal of expertise on the underlying FHE schemes, and FHE compilers that aims to lower the hurdle, generate outcomes that are typically sub-optimal as they rely on manually-developed optimization rules. In this paper, based on the prior work of FHE compilers, we propose a method for automatically learning and using optimization rules for FHE circuits. Our method focuses on reducing the maximum multiplicative depth, the decisive performance bottleneck, of FHE circuits by combining program synthesis and term rewriting. It first uses program synthesis to learn equivalences of small circuits as rewrite rules from a set of training circuits. Then, we perform term rewriting on the input circuit to obtain a new circuit that has lower multiplicative depth. Our rewriting method maximally generalizes the learned rules based on the equational matching and its soundness and termination properties are formally proven. Experimental results show that our method generates circuits that can be homomorphically evaluated 1.18x – 3.71x faster (with the geometric mean of 2.05x) than the state-of-the-art method. Our method is also orthogonal to existing domain-specific optimizations. This work was presented at ACM PLDI 2020. 

 

Bio: 

Woosuk Lee is an assistant professor of the college of computing at Hanyang University. He received a Ph.D. in Computer Science from Seoul National University in 2016. He was a postdoctoral researcher at Georgia Institute of Technology and the University of Pennsylvania from 2016-2017 and 2017-2018, respectively (homepage: http://psl.hanyang.ac.kr).

==============================