<span class="var-sub_title">Preserving Privacy through Processing Encrypted Data</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Fourth International Workshop on Heterogeneous High-Performance Reconfigurable Computing (H2RC'18)


Preserving Privacy through Processing Encrypted Data

Authors: Miriam Leeser (Northeastern University)

Abstract: Secure Function Evaluation (SFE) allows an interested party to evaluate a function over private data without learning anything about the inputs other than the outcome of this computation. This offers a strong privacy guarantee: SFE enables, e.g., a medical researcher, a statistician, or a data analyst, to conduct a study over private, sensitive data, without jeopardizing the privacy of the study's participants (patients, online users, etc.). Nevertheless, applying SFE to “big data” poses several challenges, most significantly in the excessive processing time for applications.

In this talk, I describe Garbled Circuits (GCs), a technique for implementing SFE that can be applied to any problem that can be described as a Boolean circuit. GC is a particularly good application to accelerate with FPGAs due to the good match between GC implementations and FPGA circuits. As our goal is to use GC for extremely large problems, including machine learning algorithms, we propose to address these problems by running GCs on clusters of machines equipped with FPGAs in the datacenter to accelerate the processing. In this talk, I will present our progress and challenges with this approach.





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