Differentially-Private Multi-Party Sketching for Large-Scale Statistics

Differentially-Private Multi-Party Sketching for Large-Scale Statistics

We consider a scenario where multiple organizations holding large amounts of sensitive data from their users wish to compute aggregate statistics on this data while protecting the privacy of individual users. To support large-scale analytics we investigate how this privacy can be provided for the ca...

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Bibliographic Details
Main Authors: Choi Seung Geol, Dachman-soled Dana, Kulkarni Mukul, Yerukhimovich Arkady
Format: Article
Language:English
Published: Sciendo 2020-07-01
Series:Proceedings on Privacy Enhancing Technologies
Subjects:
differential privacy
sketching algorithms
secure computation
Online Access:https://doi.org/10.2478/popets-2020-0047
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spelling doaj-f5ee3580626b46fe86d360c270c0a2f72021-09-05T14:01:10ZengSciendoProceedings on Privacy Enhancing Technologies2299-09842020-07-012020315317410.2478/popets-2020-0047popets-2020-0047Differentially-Private Multi-Party Sketching for Large-Scale StatisticsChoi Seung Geol0Dachman-soled Dana1Kulkarni Mukul2Yerukhimovich Arkady3United States Naval Academy.University of Maryland, Colleage Park.University of MassachusettsAmherst.George Washington University.We consider a scenario where multiple organizations holding large amounts of sensitive data from their users wish to compute aggregate statistics on this data while protecting the privacy of individual users. To support large-scale analytics we investigate how this privacy can be provided for the case of sketching algorithms running in time sub-linear of the input size.https://doi.org/10.2478/popets-2020-0047differential privacysketching algorithmssecure computation
collection DOAJ
language English
format Article
sources DOAJ
author Choi Seung Geol
Dachman-soled Dana
Kulkarni Mukul
Yerukhimovich Arkady
spellingShingle Choi Seung Geol
Dachman-soled Dana
Kulkarni Mukul
Yerukhimovich Arkady
Differentially-Private Multi-Party Sketching for Large-Scale Statistics
Proceedings on Privacy Enhancing Technologies
differential privacy
sketching algorithms
secure computation
author_facet Choi Seung Geol
Dachman-soled Dana
Kulkarni Mukul
Yerukhimovich Arkady
author_sort Choi Seung Geol
title Differentially-Private Multi-Party Sketching for Large-Scale Statistics
title_short Differentially-Private Multi-Party Sketching for Large-Scale Statistics
title_full Differentially-Private Multi-Party Sketching for Large-Scale Statistics
title_fullStr Differentially-Private Multi-Party Sketching for Large-Scale Statistics
title_full_unstemmed Differentially-Private Multi-Party Sketching for Large-Scale Statistics
title_sort differentially-private multi-party sketching for large-scale statistics
publisher Sciendo
series Proceedings on Privacy Enhancing Technologies
issn 2299-0984
publishDate 2020-07-01
description We consider a scenario where multiple organizations holding large amounts of sensitive data from their users wish to compute aggregate statistics on this data while protecting the privacy of individual users. To support large-scale analytics we investigate how this privacy can be provided for the case of sketching algorithms running in time sub-linear of the input size.
topic differential privacy
sketching algorithms
secure computation
url https://doi.org/10.2478/popets-2020-0047
work_keys_str_mv AT choiseunggeol differentiallyprivatemultipartysketchingforlargescalestatistics
AT dachmansoleddana differentiallyprivatemultipartysketchingforlargescalestatistics
AT kulkarnimukul differentiallyprivatemultipartysketchingforlargescalestatistics
AT yerukhimovicharkady differentiallyprivatemultipartysketchingforlargescalestatistics
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