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Using Differential Privacy to Efficiently Mitigate Side Channels in Distributed Analytics

Published: 23 April 2018 Publication History

Abstract

Distributed analytics systems enable users to efficiently perform computations over large distributed data sets. Recently, systems have been proposed that can additionally protect the data's privacy by keeping it encrypted even in memory and by performing the computations using trusted execution environments (TEEs). This approach has the potential to make it much safer to outsource analytics jobs to an untrusted cloud platform or to distribute it across multiple parties. TEEs, however, suffer from side channels, such as timing, memory access patterns, and message sizes that weaken their privacy guarantees. Existing privacy-preserving analytics systems only address a subset of these channels, such as memory access patterns, while largely neglecting size and timing. Moreover, previous attempts to close size and timing channels suffer from high performance costs, impracticality, or a lack of rigorous privacy guarantees.
In this paper, we present an approach to mitigating timing and size side channels in analytics based on differential privacy that is both dramatically more efficient than the state-of-the-art while offering principled privacy assurances. We also sketch a design for a new analytics system we are developing called Hermetic that aims to be the first to mitigate the four most critical digital side channels simultaneously. Our preliminary evaluation demonstrates the potential benefits of our method.

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  • (2023)Differentially Private Resource AllocationProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627181(772-786)Online publication date: 4-Dec-2023
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Published In

cover image ACM Conferences
EuroSec'18: Proceedings of the 11th European Workshop on Systems Security
April 2018
53 pages
ISBN:9781450356527
DOI:10.1145/3193111
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 23 April 2018

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  • Demonstration
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  • UChicago CERES Center
  • NSF

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EuroSys '18
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EuroSys '18: Thirteenth EuroSys Conference 2018
April 23 - 26, 2018
Porto, Portugal

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EuroSec'18 Paper Acceptance Rate 8 of 19 submissions, 42%;
Overall Acceptance Rate 47 of 113 submissions, 42%

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Cited By

View all
  • (2023)Differentially Private Resource AllocationProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627181(772-786)Online publication date: 4-Dec-2023
  • (2023)Efficient Bi-objective SQL Optimization for Enclaved Cloud Databases with Differentially Private PaddingACM Transactions on Database Systems10.1145/359702148:2(1-40)Online publication date: 26-Jun-2023
  • (2023)Intel Software Guard Extensions Applications: A SurveyACM Computing Surveys10.1145/359302155:14s(1-38)Online publication date: 17-Jul-2023
  • (2023)RLS Side Channels: Investigating Leakage of Row-Level Security Protected Data Through Query Execution TimeProceedings of the ACM on Management of Data10.1145/35889431:1(1-25)Online publication date: 30-May-2023
  • (2022)VizardProceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security10.1145/3548606.3559349(441-454)Online publication date: 7-Nov-2022
  • (2021)Adversarial interference and its mitigations in privacy-preserving collaborative machine learningNature Machine Intelligence10.1038/s42256-021-00390-33:9(749-758)Online publication date: 17-Sep-2021
  • (2020)SAQEProceedings of the VLDB Endowment10.14778/3407790.340785413:12(2691-2705)Online publication date: 1-Jul-2020
  • (2019)Isolation and BeyondProceedings of the Workshop on Hot Topics in Operating Systems10.1145/3317550.3321427(96-104)Online publication date: 13-May-2019
  • (2019)An Identity Privacy Preserving IoT Data Protection Scheme for Cloud Based Analytics2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006017(5744-5753)Online publication date: Dec-2019
  • (2018)ShrinkwrapProceedings of the VLDB Endowment10.14778/3291264.329127412:3(307-320)Online publication date: 1-Nov-2018

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