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DTSTART:19700308T020000
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DTSTAMP:20181221T160725Z
LOCATION:D167/174
DTSTART;TZID=America/Chicago:20181111T090100
DTEND;TZID=America/Chicago:20181111T094000
UID:submissions.supercomputing.org_SC18_sess170_pec428@linklings.com
SUMMARY:Preserving Privacy through Processing Encrypted Data
DESCRIPTION:Workshop\nAccelerators, Heterogeneous Systems, NVRAM, Workshop
  Reg Pass\n\nPreserving Privacy through Processing Encrypted Data\n\nLeese
 r\n\nSecure Function Evaluation (SFE) allows an interested party to evalua
 te 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 d
 ata analyst, to conduct a study over private, sensitive data, without jeop
 ardizing 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.\n\nI
 n this talk, I describe Garbled Circuits (GCs), a technique for implementi
 ng SFE that can be applied to any problem that can be described as a Boole
 an circuit. GC is a particularly good application to accelerate with FPGAs
  due to the good match between GC implementations and FPGA circuits. As ou
 r goal is to use GC for extremely large problems, including machine learni
 ng algorithms, we propose to address these problems by running GCs on clus
 ters of machines equipped with FPGAs in the datacenter to accelerate the p
 rocessing. In this talk, I will present our progress and challenges with t
 his approach.
URL:https://sc18.supercomputing.org/presentation/?id=pec428&sess=sess170
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