research-article

Interactive privacy via the median mechanism

Authors:

Aaron Roth,

Tim RoughgardenAuthors Info & Claims

STOC '10: Proceedings of the forty-second ACM symposium on Theory of computing

Pages 765 - 774

https://doi.org/10.1145/1806689.1806794

Published: 05 June 2010 Publication History

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Abstract

We define a new interactive differentially private mechanism --- the median mechanism --- for answering arbitrary predicate queries that arrive online. Given fixed accuracy and privacy constraints, this mechanism can answer exponentially more queries than the previously best known interactive privacy mechanism (the Laplace mechanism, which independently perturbs each query result). With respect to the number of queries, our guarantee is close to the best possible, even for non-interactive privacy mechanisms. Conceptually, the median mechanism is the first privacy mechanism capable of identifying and exploiting correlations among queries in an interactive setting.

We also give an efficient implementation of the median mechanism, with running time polynomial in the number of queries, the database size, and the domain size. This efficient implementation guarantees privacy for all input databases, and accurate query results for almost all input distributions. The dependence of the privacy on the number of queries in this mechanism improves over that of the best previously known efficient mechanism by a super-polynomial factor, even in the non-interactive setting.

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Zhao PZhang KZhang HChen H(2025)Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks modelExpert Systems with Applications10.1016/j.eswa.2024.125279259(125279)Online publication date: Jan-2025
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Jain PSmith AWagaman C(2024)Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation*2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00196(127-145)Online publication date: 19-May-2024
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Index Terms

Interactive privacy via the median mechanism
1. Theory of computation
  1. Design and analysis of algorithms

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Published In

STOC '10: Proceedings of the forty-second ACM symposium on Theory of computing

June 2010

812 pages

ISBN:9781450300506

DOI:10.1145/1806689

General Chair:
Michael Mitzenmacher
Harvard
,
Program Chair:
Leonard J. Schulman
Caltech

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2010

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STOC'10

Sponsor:

SIGACT

STOC'10: Symposium on Theory of Computing

June 5 - 8, 2010

Massachusetts, Cambridge, USA

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Overall Acceptance Rate 1,469 of 4,586 submissions, 32%

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STOC '25

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57th Annual ACM Symposium on Theory of Computing (STOC 2025)

June 23 - 27, 2025

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Cited By

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https://doi.org/10.1016/j.eswa.2024.125279
Jain PSmith AWagaman C(2024)Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation*2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00196(127-145)Online publication date: 19-May-2024
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Chang W(2024)A Real-time Privacy Monitoring System for Multi-tasks-based Privacy-disclosure in CrowdsourcingIEEE Access10.1109/ACCESS.2024.3513796(1-1)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3513796
Liu CZhi ZZhao WHe Z(2024)Research on Fingerprint Image Differential Privacy Protection Publishing Method Based on Wavelet Transform and Singular Value Decomposition TechnologyIEEE Access10.1109/ACCESS.2024.336799612(28417-28436)Online publication date: 2024
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Lu MLiu Z(2024)Improving Accuracy of Interactive Queries in Personalized Differential PrivacyFrontiers in Cyber Security10.1007/978-981-99-9331-4_10(141-159)Online publication date: 4-Jan-2024
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Das SMishra S(2024)Advances in Differential Privacy and Differentially Private Machine LearningInformation Technology Security10.1007/978-981-97-0407-1_7(147-188)Online publication date: 2-Apr-2024
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Durfee DOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Unbounded differentially private quantile and maximum estimationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669516(77691-77712)Online publication date: 10-Dec-2023
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