research-article

GUPT: privacy preserving data analysis made easy

Authors:

Prashanth Mohan,

Abhradeep Thakurta,

David CullerAuthors Info & Claims

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

Pages 349 - 360

https://doi.org/10.1145/2213836.2213876

Published: 20 May 2012 Publication History

Abstract

It is often highly valuable for organizations to have their data analyzed by external agents. However, any program that computes on potentially sensitive data risks leaking information through its output. Differential privacy provides a theoretical framework for processing data while protecting the privacy of individual records in a dataset. Unfortunately, it has seen limited adoption because of the loss in output accuracy, the difficulty in making programs differentially private, lack of mechanisms to describe the privacy budget in a programmer's utilitarian terms, and the challenging requirement that data owners and data analysts manually distribute the limited privacy budget between queries.

This paper presents the design and evaluation of a new system, GUPT, that overcomes these challenges. Unlike existing differentially private systems such as PINQ and Airavat, it guarantees differential privacy to programs not developed with privacy in mind, makes no trust assumptions about the analysis program, and is secure to all known classes of side-channel attacks.

GUPT uses a new model of data sensitivity that degrades privacy of data over time. This enables efficient allocation of different levels of privacy for different user applications while guaranteeing an overall constant level of privacy and maximizing the utility of each application. GUPT also introduces techniques that improve the accuracy of output while achieving the same level of privacy. These approaches enable GUPT to easily execute a wide variety of data analysis programs while providing both utility and privacy.

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https://doi.org/10.1007/978-981-97-2387-4_10
Epasto AMirrokni VNarayanan SZhong POh ANaumann TGloberson ASaenko KHardt MLevine S(2023)k-means clustering with distance-based privacyProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666981(19570-19593)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666981
Li YLi B(2023)Solving the Performance Issues of Epsilon Estimation Method in Differentially Private ERM: Analysis, Solution and EvaluationProceedings of the 2023 2nd International Conference on Networks, Communications and Information Technology10.1145/3605801.3605820(93-99)Online publication date: 16-Jun-2023
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GUPT: privacy preserving data analysis made easy

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cover image ACM Conferences

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

May 2012

886 pages

ISBN:9781450312479

DOI:10.1145/2213836

General Chairs:
K. Selçuk Candan
Arizona State University
,
Yi Chen
Arizona State University
,
Richard Snodgrass
University of Arizona
,
Program Chair:
Luis Gravano
Columbia University
,
Publications Chair:
Ariel Fuxman
Microsoft Research

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 May 2012

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SIGMOD/PODS '12: International Conference on Management of Data

May 20 - 24, 2012

Arizona, Scottsdale, USA

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SIGMOD '12 Paper Acceptance Rate 48 of 289 submissions, 17%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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Epasto AMirrokni VNarayanan SZhong POh ANaumann TGloberson ASaenko KHardt MLevine S(2023)k-means clustering with distance-based privacyProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666981(19570-19593)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666981
Li YLi B(2023)Solving the Performance Issues of Epsilon Estimation Method in Differentially Private ERM: Analysis, Solution and EvaluationProceedings of the 2023 2nd International Conference on Networks, Communications and Information Technology10.1145/3605801.3605820(93-99)Online publication date: 16-Jun-2023
https://dl.acm.org/doi/10.1145/3605801.3605820
Cormode G(2023)Technical Perspective on 'R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign KeysACM SIGMOD Record10.1145/3604437.360446152:1(114-114)Online publication date: 8-Jun-2023
https://dl.acm.org/doi/10.1145/3604437.3604461
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Cai KXiao XCormode G(2023)PrivLava: Synthesizing Relational Data with Foreign Keys under Differential PrivacyProceedings of the ACM on Management of Data10.1145/35892871:2(1-25)Online publication date: 20-Jun-2023
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Li YPurcell MRakotoarivelo TSmith DRanbaduge TNg K(2023)Private Graph Data Release: A SurveyACM Computing Surveys10.1145/356908555:11(1-39)Online publication date: 22-Feb-2023
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Razavi EArefi ASmith DLedwich GNourbakhsh GMinakshi M(2023)Privacy-Preserved Framework for Short-Term Probabilistic Net Energy ForecastingIEEE Transactions on Industrial Informatics10.1109/TII.2022.321132819:6(7613-7623)Online publication date: Jun-2023
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