research-article

Relationship privacy: output perturbation for queries with joins

Authors:

Vibhor Rastogi,

Dan SuciuAuthors Info & Claims

PODS '09: Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

Pages 107 - 116

https://doi.org/10.1145/1559795.1559812

Published: 29 June 2009 Publication History

Abstract

We study privacy-preserving query answering over data containing relationships. A social network is a prime example of such data, where the nodes represent individuals and edges represent relationships. Nearly all interesting queries over social networks involve joins, and for such queries, existing output perturbation algorithms severely distort query answers. We propose an algorithm that significantly improves utility over competing techniques, typically reducing the error bound from polynomial in the number of nodes to polylogarithmic. The algorithm is, to the best of our knowledge, the first to answer such queries with acceptable accuracy, even for worst-case inputs.

The improved utility is achieved by relaxing the privacy condition. Instead of ensuring strict differential privacy, we guarantee a weaker (but still quite practical) condition based on adversarial privacy. To explain precisely the nature of our relaxation in privacy, we provide a new result that characterizes the relationship between ε-indistinguishability~(a variant of the differential privacy definition) and adversarial privacy, which is of independent interest: an algorithm is ε-indistinguishable iff it is private for a particular class of adversaries (defined precisely herein). Our perturbation algorithm guarantees privacy against adversaries in this class whose prior distribution is numerically bounded.

References

[1]

L. Backstrom, C. Dwork, and J. M. Kleinberg. Wherefore art thou R3579X?: Anonymized social networks, hidden patterns, and structural steganography. In WWW, 2007.

Digital Library

[2]

H. W. Block, T. H. Savits, and M. Shaked. Some concepts of negative dependence. In Ann. of Prob., 1982.

[3]

A. Campan and T. M. Truta. A clustering approach for data and structural anonymity in social networks. In PinKDD, 2008.

[4]

C. Dwork. Differential privacy. In ICALP, 2006.

Digital Library

[5]

C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In TCC, 2006.

Digital Library

[6]

A. Evfimievski, J. Gehrke, and R. Srikant. Limiting privacy breaches in privacy preserving data mining. In PODS, 2003.

Digital Library

[7]

A. V. Evfimievski, R. Fagin, and D. P. Woodruff. Epistemic privacy. In PODS, 2008.

Digital Library

[8]

T. Feder and M. Mihail. Balanced matroids. In STOC, 1992.

Digital Library

[9]

Full version: http://www.cs.washington.edu/homes/vibhor/relationship_privacy.pdf.

[10]

S. Ganta, S. Kasiviswanathan, and A. Smith. Composition attacks and auxiliary information in data privacy. In KDD, 2008.

Digital Library

[11]

M. Hay, G. Miklau, D. Jensen, D. Towsley, and P. Weis. Resisting structural re-identification in anonymized social networks. In VLDB, 2008.

Digital Library

[12]

K. Liu and E. Terzi. Towards identity anonymization on graphs. In SIGMOD, 2008.

Digital Library

[13]

A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. In ICDE, 2006.

Digital Library

[14]

G. Miklau and D. Suciu. A formal analysis of information disclosure in data exchange. In SIGMOD, 2004.

Digital Library

[15]

M. Newman. The structure and function of complex networks. SIREV: SIAM Review, 2003.

[16]

K. Nissim, S. Raskhodnikova, and A. Smith. Smooth sensitivity and sampling in private data analysis. In STOC, 2007.

Digital Library

[17]

V. Rastogi, D. Suciu, and S. Hong. The boundary between privacy and utility in data publishing. In VLDB, 2007.

Digital Library

[18]

J. G. Shanthikumar and H.-W. Koo. On uniform conditional stochastic order conditioned on planar regions. In Ann. of Probab., 1990.

[19]

V. Vu. Concentration of non-lipschitz functions and applications. RSA, 2002.

Digital Library

[20]

X. Ying and X. Wu. Randomizing social networks: a spectrum preserving approach. In SIAM, 2007.

[21]

E. Zheleva and L. Getoor. Preserving the privacy of sensitive relationships in graph data. In PinKDD, 2007.

Digital Library

[22]

B. Zhou and J. Pei. Preserving privacy in social networks against neighborhood attacks. In ICDE, 2008.

Digital Library

Cited By

Yang HShi DZhang YXu YXu K(2024)An Efficient Local Differential Privacy Approach for Trajectory Publishing with High UtilityDatabase Systems for Advanced Applications10.1007/978-981-97-5562-2_5(71-88)Online publication date: 27-Oct-2024
https://doi.org/10.1007/978-981-97-5562-2_5
Rahimian MYu FHurtado CAgmon NAn BRicci AYeoh W(2023)Differentially Private Network Data Collection for Influence MaximizationProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3599081(2795-2797)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3599081
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
https://dl.acm.org/doi/10.1145/3569085
Show More Cited By

Index Terms

Relationship privacy: output perturbation for queries with joins

Recommendations

Optimizing linear counting queries under differential privacy
PODS '10: Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

Differential privacy is a robust privacy standard that has been successfully applied to a range of data analysis tasks. But despite much recent work, optimal strategies for answering a collection of related queries are not known.

We propose the matrix ...
Bayesian Differential Privacy on Correlated Data
SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

Differential privacy provides a rigorous standard for evaluating the privacy of perturbation algorithms. It has widely been regarded that differential privacy is a universal definition that deals with both independent and correlated data and a ...
Smooth sensitivity and sampling in private data analysis
STOC '07: Proceedings of the thirty-ninth annual ACM symposium on Theory of computing

We introduce a new, generic framework for private data analysis.The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains.Our framework ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

PODS '09: Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

June 2009

298 pages

ISBN:9781605585536

DOI:10.1145/1559795

General Chair:
Jan Paredaens
University of Antwerp, Belgium
,
Program Chair:
Jianwen Su
University of California, Santa Barbara, USA

Copyright © 2009 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGMOD/PODS '09

Sponsor:

SIGMOD/PODS '09: International Conference on Management of Data

June 29 - July 1, 2009

Rhode Island, Providence, USA

Acceptance Rates

PODS '09 Paper Acceptance Rate 26 of 97 submissions, 27%;

Overall Acceptance Rate 642 of 2,707 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

83
Total Citations
View Citations
627
Total Downloads

Downloads (Last 12 months)26
Downloads (Last 6 weeks)3

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yang HShi DZhang YXu YXu K(2024)An Efficient Local Differential Privacy Approach for Trajectory Publishing with High UtilityDatabase Systems for Advanced Applications10.1007/978-981-97-5562-2_5(71-88)Online publication date: 27-Oct-2024
https://doi.org/10.1007/978-981-97-5562-2_5
Rahimian MYu FHurtado CAgmon NAn BRicci AYeoh W(2023)Differentially Private Network Data Collection for Influence MaximizationProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3599081(2795-2797)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3599081
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
https://dl.acm.org/doi/10.1145/3569085
Cohen-Addad VFan CLattanzi SMitrović SNorouzi-Fard AParotsidis NTarnawski JKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Near-optimal correlation clustering with privacyProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602712(33702-33715)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602712
Dong WYi KLibkin LBarceló P(2022)A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive QueriesProceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3517804.3524143(213-225)Online publication date: 12-Jun-2022
https://dl.acm.org/doi/10.1145/3517804.3524143
Fan CLi P(2022)Distances Release with Differential Privacy in Tree and Grid Graph2022 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT50566.2022.9834836(2190-2195)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1109/ISIT50566.2022.9834836
Odeyomi O(2022)Differential Privacy in Social Networks Using Multi-Armed BanditIEEE Access10.1109/ACCESS.2022.314408410(11817-11829)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3144084
Miao XZhang WJiang YFu FShao YChen LTao YCao GCui B(2022)PCG: a privacy preserving collaborative graph neural network training frameworkThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00768-832:4(717-736)Online publication date: 4-Nov-2022
https://dl.acm.org/doi/10.1007/s00778-022-00768-8
Pejó BDesfontaines DPejó BDesfontaines D(2022)Scope and Related WorkGuide to Differential Privacy Modifications10.1007/978-3-030-96398-9_11(79-87)Online publication date: 10-Apr-2022
https://doi.org/10.1007/978-3-030-96398-9_11
Pejó BDesfontaines DPejó BDesfontaines D(2022)Summarizing TableGuide to Differential Privacy Modifications10.1007/978-3-030-96398-9_10(59-77)Online publication date: 10-Apr-2022
https://doi.org/10.1007/978-3-030-96398-9_10
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten