CSAIL Event Calendar

Techniques for performing secure computation on encrypted data

Abstract: Privacy of data is a huge problem in cloud computing, and more generally in outsourcing computation. From financial information to medical records, sensitive data is stored and computed upon in the cloud. Computation requires the data to be exposed to the cloud servers, which may be attacked by malicious applications, hypervisors, operating systems or insiders.
In the ideal scenario, no one other than the user sees the private data in decrypted form, as is achieved through the use of fully homomorphic encryption (FHE) techniques. The first part of the talk will focus on (a) techniques to run general purpose programs under FHE and (b) how some programs are naturally better suited for FHE than others. I will talk about how ambiguity in program control flow and data structures leads to large overheads for certain programs, in addition to the crypto overheads already imposed by FHE (which imposes about a billion times slowdown).

Motivated by large FHE overheads, the rest of the talk describes two schemes that approximate FHE with a minimal trusted computing base (TCB) made out of secure hardware. The first scheme's TCB is a simple hardware unit (an ALU, plus commodity encrypt/decrypt logic) that performs arithmetic operations faster than FHE but still suffers from the program/data ambiguity problem. To address the ambiguity problem, the second scheme's TCB is a secure processor that uses oblivious RAM techniques (to obfuscate external requests) and specialized circuits (to obfuscate internal behavior). Surprisingly, this secure processor incurs only a ~5X performance overhead. In both schemes, no software (the user application, server operating system, etc) or anything outside the hardware unit (external RAM or communication channels) is trusted.

Joint work with Marten van Dijk and Srini Devadas.

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