research-article

Differential privacy in new settings

Author:

Cynthia DworkAuthors Info & Claims

SODA '10: Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete algorithms

Pages 174 - 183

Published: 17 January 2010 Publication History

Abstract

Differential privacy is a recent notion of privacy tailored to the problem of statistical disclosure control: how to release statistical information about a set of people without compromising the the privacy of any individual [7].

We describe new work [10, 9] that extends differentially private data analysis beyond the traditional setting of a trusted curator operating, in perfect isolation, on a static dataset. We ask

• How can we guarantee differential privacy, even against an adversary that has access to the algorithm's internal state, eg, by subpoena? An algorithm that achives this is said to be pan-private.

• How can we guarantee differential privacy when the algorithm must continually produce outputs? We call this differential privacy under continual observation.

We also consider these requirements in conjunction.

References

[1]

A. Blum, C. Dwork, F. McSherry, and K. Nissim. Practical privacy: The SuLQ framework. In Proceedings of the 24th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, June 2005.

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[2]

I. Dinur and K. Nissim. Revealing information while preserving privacy. In Proceedings of the Twenty-Second ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 202--210, 2003.

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[3]

C. Dwork. A firm foundation for private data analysis. Communications of the ACM (to appear).

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[4]

C. Dwork. An ad omnia approach to defining and achieving private data analysis. In F. Bonchi, E. Ferrari, B. Malin, and Y. Saygin, editors, Privacy, Security, and Trust in KDD, First ACM SIGKDD International (PinKDD), Revised Selected Papers, volume 4890 of Lecture Notes in Computer Science, pages 1--13. Springer, 2007.

Digital Library

[5]

C. Dwork. The differential privacy frontier. In Proceedings of the 6th Theory of Cryptography Conference (TCC), 2009.

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[6]

C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor. Our data, ourselves: privacy via distributed noise generation. In Advances in Cryptology: Proceedings of EUROCRYPT, pages 486--503, 2006.

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[7]

C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In Proceedings of the 3rd Theory of Cryptography Conference, pages 265--284, 2006.

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[8]

C. Dwork, F. McSherry, and K. Talwar. The price of privacy and the limits of lp decoding. In Proceedings of the 39th ACM Symposium on Theory of Computing, pages pp. 85--94, 2007.

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[9]

C. Dwork, M. Naor, T. Pitassi, and G. Rothblum. Differential privacy under continual observation. Manuscript in preparation, 2009.

[10]

C. Dwork, M. Naor, T. Pitassi, G. Rothblum, and S. Yekhanin. Pan-private streaming algorithms. Manuscript submitted for publication, 2009.

[11]

C. Dwork and K. Nissim. Privacy-preserving datamining on vertically partitioned databases. In Proceedings of CRYPTO 2004, volume 3152, pages 528--544, 2004.

[12]

C. Dwork and S. Yekhanin. New efficient attacks on statistical disclosure control mechanisms. In Proceedings of CRYPTO 2008, pages 468--480, 2008.

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M. Hardt and K. Talwar. On the geometry of differential privacy. arXiv:0907.3754v2, 2009.

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F. McSherry and K. Talwar. Mechanism design via differential privacy. In Proceedings of the 48th Annual Symposium on Foundations of Computer Science, 2007.

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S. Warner. Randomized response: a survey technique for eliminating evasive answer bias. JASA, pages 63--69, 1965.

Cited By

Wang CPujol DNayak KMachanavajjhala ACalandrino JTroncoso C(2023)Private proof-of-stake blockchains using differentially-private stake distortionProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620326(1577-1594)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620326
Wei CZhao MZhang ZChen MMeng WLiu BFan YChen WMeng WJensen CCremers CKirda E(2023)DPMLBench: Holistic Evaluation of Differentially Private Machine LearningProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3616593(2621-2635)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3576915.3616593
Azamuke DKatarahweire MBainomugisha E(2022)Scenario-based Synthetic Dataset Generation for Mobile Money TransactionsProceedings of the Federated Africa and Middle East Conference on Software Engineering10.1145/3531056.3542774(64-72)Online publication date: 7-Jun-2022
https://dl.acm.org/doi/10.1145/3531056.3542774
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Information

Published In

cover image ACM Conferences

SODA '10: Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete algorithms

January 2010

1690 pages

ISBN:9780898716986

Program Chair:
Moses Charikar
Princeton University

Sponsors

SIAM Activity Group on Discrete Mathematics
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory

Publisher

Society for Industrial and Applied Mathematics

United States

Publication History

Published: 17 January 2010

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SODA '10 Paper Acceptance Rate 135 of 445 submissions, 30%;

Overall Acceptance Rate 411 of 1,322 submissions, 31%

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Cited By

Wang CPujol DNayak KMachanavajjhala ACalandrino JTroncoso C(2023)Private proof-of-stake blockchains using differentially-private stake distortionProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620326(1577-1594)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620326
Wei CZhao MZhang ZChen MMeng WLiu BFan YChen WMeng WJensen CCremers CKirda E(2023)DPMLBench: Holistic Evaluation of Differentially Private Machine LearningProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3616593(2621-2635)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3576915.3616593
Azamuke DKatarahweire MBainomugisha E(2022)Scenario-based Synthetic Dataset Generation for Mobile Money TransactionsProceedings of the Federated Africa and Middle East Conference on Software Engineering10.1145/3531056.3542774(64-72)Online publication date: 7-Jun-2022
https://dl.acm.org/doi/10.1145/3531056.3542774
Zhang WYin GSha YYang J(2021)Protecting the Moving User’s Locations by Combining Differential Privacy and k-Anonymity under Temporal Correlations in Wireless NetworksWireless Communications & Mobile Computing10.1155/2021/66919752021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6691975
Wei HGe LLu ZZhang GQin D(2021)Differential Privacy Image Publishing based on NMF and SVDProceedings of the 2021 13th International Conference on Bioinformatics and Biomedical Technology10.1145/3473258.3473266(48-54)Online publication date: 21-May-2021
https://dl.acm.org/doi/10.1145/3473258.3473266
Wang CBater JNayak KMachanavajjhala ALi GLi ZIdreos SSrivastava D(2021)DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential PrivacyProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457306(1892-1905)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457306
Kasyap HTripathy S(2021)Privacy-preserving Decentralized Learning Framework for Healthcare SystemACM Transactions on Multimedia Computing, Communications, and Applications10.1145/342647417:2s(1-24)Online publication date: 14-Jun-2021
https://dl.acm.org/doi/10.1145/3426474
Assefa SDervovic DMahfouz MTillman RReddy PVeloso MBalch T(2020)Generating synthetic data in financeProceedings of the First ACM International Conference on AI in Finance10.1145/3383455.3422554(1-8)Online publication date: 15-Oct-2020
https://dl.acm.org/doi/10.1145/3383455.3422554
Fioretto FVan Hentenryck P(2019)OptstreamJournal of Artificial Intelligence Research10.1613/jair.1.1158365:1(423-456)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1613/jair.1.11583
Lècuyer MSpahn RVodrahalli KGeambasu RHsu D(2019)Privacy Accounting and Quality Control in the Sage Differentially Private ML PlatformACM SIGOPS Operating Systems Review10.1145/3352020.335203253:1(75-84)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3352020.3352032
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