TY - GEN
T1 - Doing real work with FHE
T2 - 6th Annual Workshop on Encrypted Computing and Applied Homomorphic Cryptography. WAHC 208, co-located with CCS 2018
AU - Crawford, Jack L.H.
AU - Gentry, Craig
AU - Halevi, Shai
AU - Platt, Daniel
AU - Shoup, Victor
PY - 2018/10/15
Y1 - 2018/10/15
N2 - We describe our recent experience, building a system that uses fully-homomorphic encryption (FHE) to approximate the coefficients of a logistic-regression model, built from genomic data. The aim of this project was to examine the feasibility of a solution that operates "deep within the bootstrapping regime," solving a problem that appears too hard to be addressed just with somewhat-homomorphic encryption. As part of this project, we implemented optimized versions of many bread and butter FHE tools. These tools include binary arithmetic, comparisons, partial sorting, and low-precision approximation of arbitrary functions (used for reciprocals, logarithms, etc.). Our solution can handle thousands of records and hundreds of fields, and it takes a few hours to run. To achieve this performance we had to be extremely frugal with expensive bootstrapping and data-movement operations. We believe that our experience in this project could serve as a guide for what is or is not currently feasible to do with fully-homomorphic encryption.
AB - We describe our recent experience, building a system that uses fully-homomorphic encryption (FHE) to approximate the coefficients of a logistic-regression model, built from genomic data. The aim of this project was to examine the feasibility of a solution that operates "deep within the bootstrapping regime," solving a problem that appears too hard to be addressed just with somewhat-homomorphic encryption. As part of this project, we implemented optimized versions of many bread and butter FHE tools. These tools include binary arithmetic, comparisons, partial sorting, and low-precision approximation of arbitrary functions (used for reciprocals, logarithms, etc.). Our solution can handle thousands of records and hundreds of fields, and it takes a few hours to run. To achieve this performance we had to be extremely frugal with expensive bootstrapping and data-movement operations. We believe that our experience in this project could serve as a guide for what is or is not currently feasible to do with fully-homomorphic encryption.
KW - Homomorphic encryption
KW - Implementation
KW - Logistic regression
KW - Private genomic computation
UR - http://www.scopus.com/inward/record.url?scp=85056810838&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056810838&partnerID=8YFLogxK
U2 - 10.1145/3267973.3267974
DO - 10.1145/3267973.3267974
M3 - Conference contribution
AN - SCOPUS:85056810838
T3 - Proceedings of the ACM Conference on Computer and Communications Security
SP - 1
EP - 12
BT - WAHC 2018 - Proceedings of the 6th Workshop on Encrypted Computing and Applied Homomorphic Cryptography, co-located with CCS 2018
PB - Association for Computing Machinery
Y2 - 19 October 2018
ER -