research-article

Privacy-preserving social network analysis for criminal investigations

Authors:

Florian Kerschbaum,

Andreas SchaadAuthors Info & Claims

WPES '08: Proceedings of the 7th ACM workshop on Privacy in the electronic society

Pages 9 - 14

https://doi.org/10.1145/1456403.1456406

Published: 27 October 2008 Publication History

Abstract

Social network analysis (SNA) is now a commonly used tool in criminal investigations, but evidence gathering and analysis is often restricted by data privacy laws. We consider the case where multiple investigators want to collaborate, but do not yet have sufficient evidence that justifies a plaintext data exchange. This paper proposes a solution for privacy-preserving social network analysis where several investigators can collaborate without actually exchanging sensitive private information. An investigator can request data from other sites to augment his view without revealing personally identifiable data. The investigator can compute important metrics by means of a SNA on the subject while keeping the entire social network unknown him.

References

[1]

Directive 95--46-EC on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Available at http://ec.europa.eu/justice home/fsj/privacy, 1995.

[2]

IBM Entity Analytic Solutions. Available at http://ibm.com/db2/eas, 2005.

[3]

M. Atallah, F. Kerschbaum, and W. Du. Secure and Private Sequence Comparisons. Proceedings of the 2nd annual Workshop on Privacy in the Electronic Society, 2003.

Digital Library

[4]

M. Ben-Or, and A. Wigderson. Completeness theorems for non-cryptographic fault-tolerant distributed computation. Proceedings of the 20th annual ACM symposium on Theory of computing, 1988.

Digital Library

[5]

J. Brickell, and V. Shmatikov. Privacy-Preserving Graph Algorithms in the Semi-honest Model. Proceedings of AsiaCrypt, 2005.

Digital Library

[6]

R. Cramer, I. Damgard, and U. Maurer. General Secure Multi-party Computation from any Linear Secret-Sharing Scheme. Proceedings of EuroCrypt, 2000.

[7]

R. Cramer, I. Damgard, and J. Nielsen. Multiparty Computation from Threshold Homomorphic Encryption. Proceedings of EuroCrypt, 2001.

Digital Library

[8]

J. Canny. Collaborative Filtering with Privacy. Proceedings of the IEEE Symposium on Security and Privacy, 2002.

Digital Library

[9]

J. Canny. Collaborative Filtering with Privacy via Factor Analysis. Proceedings of the 25th International ACM Conference on Research and Development in Information Retrieval, 2002.

Digital Library

[10]

I. Damgard, M. Fitzi, E. Kiltz, J. Nielsen, and T. Toft. Unconditionally Secure Constant-Rounds Multi-party Computation for Equality, Comparison, Bits and Exponentiation. Proceedings of Theoretical Cryptography Conference, 2006.

Digital Library

[11]

I. Damgard, and M. Jurik. A Generalisation, a Simplification and Some Applications of Paillier's Probabilistic Public-Key System. Proceedings of Public Key Cryptography, 2001.

Digital Library

[12]

Y. Duan, J. Wang, M. Kam, and J. Canny. Privacy Preserving Link Analysis on Dynamic Weighted Graph. Computational & Mathematical Organization Theory 11(2), 2005.

Digital Library

[13]

R. Floyd. Algorithm 97: Shortest Path. Communications of the ACM 5(6), 1962.

Digital Library

[14]

K. Frikken. Privacy-Preserving Set Union. Proceedings of Applied Cryptography and Network Security, 2007.

Digital Library

[15]

K. Frikken, and P. Golle. Private Social Network Analysis: How to Assemble Pieces of a Graph Privately. Proceedings of the ACM Workshop on Privacy in the Electronic Society, 2006.

Digital Library

[16]

O. Goldreich. Secure Multi-party Computation. Available at www.wisdom.weizmann.ac.il/?oded/pp.html, 2002.

[17]

O. Goldreich, S. Micali, and A. Wigderson. How to play any mental game. Proceedings of the 19th annual ACM conference on Theory of computing, 1987.

Digital Library

[18]

W. Harper, and D. Harris. The application of link analysis to police intelligence. Human Factors 17(2), 1975.

[19]

L. Kissner, and D. Song. Privacy-Preserving Set Operations. Proceedings of CRYPTO, 2005.

[20]

P. Paillier. Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. Proceedings of EUROCRYPT, 1999.

Digital Library

[21]

S. Pohlig, and M. Hellman. An improved algorithm for computing logarithms over GF(p) and its cryptographic significance. IEEE Transactions on Information Theory 24, 1978.

[22]

H. Polat, and W. Du. Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques. Proceedings of the 3rd IEEE International Conference on Data Mining, 2003.

Digital Library

[23]

M. Sparrow. The application of network analysis to criminal intelligence: an assessment of the prospects. Social Networks 13, 1991.

[24]

T. Toft. Primitives and Applications for Multi-party Computation. PhD dissertation, University of Aarhus, 2007.

[25]

T. Van Cangh, A. Boujraf. The Eurojust-Europol Case Study. Available at http://www.r4egov.eu/resources/details.php?Id taxonomy=6, 2007.

[26]

J. Xu, and H. Chen. Criminal Network Analysis and Visualization. Communications of the ACM 48(6), 2005.

Digital Library

[27]

A. Yao. Protocols for Secure Computations. Proceedings of the annual IEEE Symposium on Foundations of Computer Science 23, 1982.

Digital Library

Cited By

Kukkala VIyengar S(2020)Identifying Influential Spreaders in a Social Network (While Preserving Privacy)Proceedings on Privacy Enhancing Technologies10.2478/popets-2020-00402020:2(537-557)Online publication date: 8-May-2020
https://doi.org/10.2478/popets-2020-0040
Kukkala VChampin PGandon FMédini LLalmas MIpeirotis P(2018)Privacy Preserving Distributed Analysis of Social NetworksCompanion Proceedings of the The Web Conference 201810.1145/3184558.3186578(873-877)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3186578
Kukkala VIyengar S(2018)Computing Betweenness Centrality: An Efficient Privacy-Preserving ApproachCryptology and Network Security10.1007/978-3-030-00434-7_2(23-42)Online publication date: 1-Sep-2018
https://doi.org/10.1007/978-3-030-00434-7_2
Show More Cited By

Index Terms

Privacy-preserving social network analysis for criminal investigations
1. Security and privacy
  1. Cryptography
  2. Systems security
    1. Operating systems security
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

Recommendations

Privacy-preserving data sharing in cloud computing

Storing and sharing databases in the cloud of computers raise serious concern of individual privacy. We consider two kinds of privacy risk: presence leakage, by which the attackers can explicitly identify individuals in (or not in) the database, and ...
Preserving Genomic Privacy via Selective Sharing
WPES'20: Proceedings of the 19th Workshop on Privacy in the Electronic Society

Although genomic data has significant impact and widespread usage in medical research, it puts individuals' privacy in danger, even if they anonymously or partially share their genomic data. To address this problem, we present a framework that is ...
Preserving Structural Properties in Edge-Perturbing Anonymization Techniques for Social Networks

Social networks are attracting significant interest from researchers in different domains, especially with the advent of social networking systems which enable large-scale collection of network information. However, as much as analysis of such social ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WPES '08: Proceedings of the 7th ACM workshop on Privacy in the electronic society

October 2008

128 pages

ISBN:9781605582894

DOI:10.1145/1456403

General Chair:
Vijay Atluri
Rutgers University, USA
,
Program Chair:
Marianne Winslett
University of Illinois at Urbana-Champaign, USA

Copyright © 2008 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: 27 October 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CCS08

Sponsor:

CCS08: 15th ACM Conference on Computer and Communications Security 2008

October 27, 2008

Virginia, Alexandria, USA

Acceptance Rates

Overall Acceptance Rate 106 of 355 submissions, 30%

Upcoming Conference

CCS '25

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
947
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)2

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kukkala VIyengar S(2020)Identifying Influential Spreaders in a Social Network (While Preserving Privacy)Proceedings on Privacy Enhancing Technologies10.2478/popets-2020-00402020:2(537-557)Online publication date: 8-May-2020
https://doi.org/10.2478/popets-2020-0040
Kukkala VChampin PGandon FMédini LLalmas MIpeirotis P(2018)Privacy Preserving Distributed Analysis of Social NetworksCompanion Proceedings of the The Web Conference 201810.1145/3184558.3186578(873-877)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3186578
Kukkala VIyengar S(2018)Computing Betweenness Centrality: An Efficient Privacy-Preserving ApproachCryptology and Network Security10.1007/978-3-030-00434-7_2(23-42)Online publication date: 1-Sep-2018
https://doi.org/10.1007/978-3-030-00434-7_2
Pramanik MLau RYue WYe YLi C(2017)Big data analytics for security and criminal investigationsWIREs Data Mining and Knowledge Discovery10.1002/widm.12087:4Online publication date: 12-May-2017
https://doi.org/10.1002/widm.1208
Thuraisingham BAbrol SHeatherly RKantarcioglu MKhadilkar VKhan L(2016)Social Network Integration and Analysis with Privacy PreservationAnalyzing and Securing Social Networks10.1201/b19566-43(459-475)Online publication date: 31-Mar-2016
https://doi.org/10.1201/b19566-43
Kukkala VSaini JIyengar S(2016)Privacy Preserving Network Analysis of Distributed Social NetworksInformation Systems Security10.1007/978-3-319-49806-5_18(336-355)Online publication date: 24-Nov-2016
https://doi.org/10.1007/978-3-319-49806-5_18
Hassani HHuang XSilva EGhodsi M(2016)A review of data mining applications in crimeStatistical Analysis and Data Mining10.1002/sam.113129:3(139-154)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1002/sam.11312
Kwahk KKang E(2015)Management of duplicate members on websitesProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201510.1145/2808797.2808815(1104-1109)Online publication date: 25-Aug-2015
https://dl.acm.org/doi/10.1145/2808797.2808815
Yuan MChen LYu PMei H(2015)Privacy preserving graph publication in a distributed environmentWorld Wide Web10.1007/s11280-014-0290-418:5(1481-1517)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s11280-014-0290-4
Yang CThuraisingham B(2014)A Generalized Approach for Social Network Integration and Analysis with Privacy PreservationData Mining and Knowledge Discovery for Big Data10.1007/978-3-642-40837-3_8(259-280)Online publication date: 2014
https://doi.org/10.1007/978-3-642-40837-3_8
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