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DTSTART;TZID=America/Chicago:20181111T090100
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UID:submissions.supercomputing.org_SC18_sess170_pec428@linklings.com
SUMMARY:Preserving Privacy through Processing Encrypted Data
DESCRIPTION:Miriam Leeser (Northeastern University)\n\nSecure Function Eva
 luation (SFE) allows an interested party to evaluate a function over priva
 te 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, app
 lying SFE to “big data” poses several challenges, most significantly in th
 e excessive processing time for applications.\n\nIn this talk, I describe 
 Garbled Circuits (GCs), a technique for implementing SFE that can be appli
 ed to any problem that can be described as a Boolean circuit. GC is a part
 icularly good application to accelerate with FPGAs due to the good match b
 etween GC implementations and FPGA circuits. As our goal is to use GC for 
 extremely large problems, including machine learning algorithms, we propos
 e to address these problems by running GCs on clusters of machines equippe
 d with FPGAs in the datacenter to accelerate the processing. In this talk,
  I will present our progress and challenges with this approach.\n\nTag: Ac
 celerators, Heterogeneous Systems, NVRAM\n\nRegistration Category: Worksho
 p Reg Pass\n\n
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