research-article

Secure Centrality Computation Over Multiple Networks

Authors:

Francesco Bonchi,

David Garcia-Soriano,

Tamir TassaAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 957 - 966

https://doi.org/10.1145/3038912.3052602

Published: 03 April 2017 Publication History

Abstract

Consider a multi-layered graph, where the different layers correspond to different proprietary social networks on the same ground set of users. Suppose that the owners of the different networks (called hosts) are mutually non-trusting parties: how can they compute a centrality score for each of the users using all the layers, but without disclosing information about their private graphs?

Under this setting we study a suite of three centrality measures whose algebraic structure allows performing that computation with provable security and efficiency. The first measure counts the nodes reachable from a node within a given radius. The second measure extends the first one by counting the number of paths between any two nodes. The final one is a generalization to the multi-layered graph case: not only the number of paths is counted, but also the multiplicity of these paths in the different layers is considered.

We devise a suite of multiparty protocols to compute those centrality measures, which are all provably secure in the information-theoretic sense. One typical challenge and limitation of secure multiparty computation protocols is their scalability. We tackle this problem and devise a protocol which is highly scalable and still provably secure. We test our protocols on several real-world multi-layered graphs: interestingly, the protocol to compute the most sensitive measure (i.e., the multi-layered centrality) is also the most scalable one and can be efficiently run on very large networks.

References

[1]

G. Asharov and Y. Lindell. A full proof of the BGW protocol for perfectly secure multiparty computation. J. Cryptology, 30(1):58--151, 2017.

Digital Library

[2]

G. Asharov, Y. Lindell, T. Schneider, and M. Zohner. More efficient oblivious transfer and extensions for faster secure computation. In Conference on Computer and Communications Security, pages 535--548, 2013.

Digital Library

[3]

M. Ben-Or, S. Goldwasser, and A. Wigderson. Completeness theorems for non-cryptographic fault-tolerant distributed computation. In STOC, pages 1--10, 1988.

Digital Library

[4]

P. Boldi and S. Vigna. Axioms for centrality. Internet Mathematics, 10(3--4):222--262, 2014.

[5]

S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks, 30(1--7):107--117, 1998.

Digital Library

[6]

M. Burkhart, M. Strasser, D. Many, and X. A. Dimitropoulos. SEPIA: privacy-preserving aggregation of multi-domain network events and statistics. In USENIX, pages 223--240, 2010.

Digital Library

[7]

R. Canetti. Security and composition of multiparty cryptographi protocols. J. Cryptology, 13(1):143--202, 2000.

Digital Library

[8]

D. Chaum, C. Crépeau, and I. Damgård. Multiparty unconditionally secure protocols. In STOC, pages 11--19, 1988.

Digital Library

[9]

M. De Domenico, A. Solé-Ribalta, S. Gímez, and A. Arenas. Navigability of interconnected networks under random failures. Proceedings of the National Academy of Sciences, 111(23):8351--8356, 2014.

[10]

D. Demmler, T. Schneider, and M. Zohner. Ad-hoc secure two-party computation on mobile devices using hardware tokens. In USENIX Security Symposium, pages 893--908, 2014.

Digital Library

[11]

K. C. Foster, S. Q. Muth, J. J. Potterat, and R. B. Rothenberg. A faster katz status score algorithm. Computational & Mathematical Organization Theory, 7(4):275--285, 2001.

Digital Library

[12]

R. Gennaro, M. O. Rabin, and T. Rabin. Simplified VSS and fact-track multiparty computations with applications to threshold cryptography. In PODC, pages 101--111, 1998.

Digital Library

[13]

O. Goldreich. The Foundations of Cryptography - Volume 2, Basic Applications. Cambridge University Press, 2004.

[14]

O. Goldreich, S. Micali, and A. Wigderson. How to play any mental game or A completeness theorem for protocols with honest majority. In STOC, pages 218--229, 1987.

Digital Library

[15]

S. Goldwasser and S. Micali. Probabilistic encryption. J. Comput. Syst. Sci., 28(2):270--299, 1984.

[16]

S. Goldwasser, S. Micali, and C. Rackoff. The knowledge complexity of interactive proof systems. SIAM J. Comput., 18(1):186--208, 1989.

Digital Library

[17]

A. Halu, R. J. Mondragón, P. Panzarasa, and G. Bianconi. Multiplex pagerank. PloS one, 8(10):e78293, 2013.

[18]

M. Kantarcioglu and C. Clifton. Privacy-preserving distributed mining of association rules on horizontally partitioned data. Transactions on Knowledge and Data Engineering, 16(9):1026--1037, 2004.

Digital Library

[19]

L. Katz. A new status index derived from sociometric index. Psychometrika, pages 39--43, 1953.

[20]

M. Kivelä, A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno, and M. A. Porter. Multilayer networks. Journal of Complex Networks, 2(3):203--271, 2014.

[21]

N. Korula and S. Lattanzi. An efficient reconciliation algorithm for social networks. PVLDB, 7(5):377--388, 2014.

Digital Library

[22]

D. Liben-Nowell and J. M. Kleinberg. The link prediction problem for social networks. In CIKM, pages 556--559, 2003.

Digital Library

[23]

X. Lin, C. Clifton, and M. Zhu. Privacy-preserving clustering with distributed EM mixture modeling. Knowl. Inf. Syst., 8(1):68--81, 2005.

Digital Library

[24]

Y. Lindell and B. Pinkas. Privacy preserving data mining. In Crypto, pages 36--54, 2000.

Digital Library

[25]

Y. Lindell and B. Pinkas. A proof of security of yao's protocol for two-party computation. J. Cryptology, 22(2):161--188, 2009.

Digital Library

[26]

Z. Lu, B. Savas, W. Tang, and I. S. Dhillon. Supervised link prediction using multiple sources. In ICDM, pages 923--928, 2010.

Digital Library

[27]

D. Malkhi, N. Nisan, B. Pinkas, and Y. Sella. Fairplay - secure two-party computation system. In USENIX Security Symposium, pages 287--302, 2004.

Digital Library

[28]

M. J. Rattigan and D. Jensen. The case for anomalous lin discovery. SIGKDD Explor. Newsl., 7(2):41--47, 2005.

Digital Library

[29]

P. Sarkar and A. W. Moore. A tractable approach to finding closest truncated-commute-time neighbors in large graphs. In UAI, pages 335--343, 2007.

Digital Library

[30]

A. Shamir. How to share a secret. Commun. ACM, 22(11):612--613, 1979.

Digital Library

[31]

A. Solé-Ribalta, M. De Domenico, S. Gómez, and A. Arenas. Centrality rankings in multiplex networks. In WebSci, pages 149--155, 2014.

Digital Library

[32]

D. R. Stinson. Cryptography - theory and practice. Discrete mathematics and its applications series. Chapman & Hall/CRC, 2006.

Digital Library

[33]

T. Tassa. Secure mining of association rules inhorizontally distributed databases. Transactions on Knowledge and Data Engineering, 26(4):970--983, 2014.

Digital Library

[34]

J. Vaidya and C. Clifton. Privacy preserving association rule mining in vertically partitioned data. In KDD, pages 639--644, 2002.

Digital Library

[35]

C. Wang, V. Satuluri, and S. Parthasarathy. Local probabilistic models for link prediction. In ICDM, pages 322--331, 2007.

Digital Library

[36]

A. Yao. Protocols for secure computation. In FOCS, pages 160--164, 1982.

Digital Library

[37]

A. C. Yao. How to generate and exchange secrets. In FOCS, pages 162--167, 1986.

Digital Library

[38]

J. Zhan, S. Matwin, and L. Chang. Privacy preserving collaborative association rule mining. In Data and Applications Security, pages 153--165, 2005.

Digital Library

Cited By

Pessach DTassa TShmueli E(2024)Fairness-Driven Private Collaborative Machine LearningACM Transactions on Intelligent Systems and Technology10.1145/363936815:2(1-30)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3639368
Tassa THorin A(2022)Privacy Preserving Collaborative Filtering by Distributed MediationACM Transactions on Intelligent Systems and Technology10.1145/3542950Online publication date: 6-Jun-2022
https://doi.org/10.1145/3542950
Shmueli ETassa T(2020)Mediated Secure Multi-Party Protocols for Collaborative FilteringACM Transactions on Intelligent Systems and Technology10.1145/337540211:2(1-25)Online publication date: 24-Feb-2020
https://doi.org/10.1145/3375402
Show More Cited By

Index Terms

Secure Centrality Computation Over Multiple Networks
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
        Network data models
  2. World Wide Web
    1. Web applications
      1. Social networks
2. Security and privacy

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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WWW '17

Sponsor:

IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

Acceptance Rates

WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Pessach DTassa TShmueli E(2024)Fairness-Driven Private Collaborative Machine LearningACM Transactions on Intelligent Systems and Technology10.1145/363936815:2(1-30)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3639368
Tassa THorin A(2022)Privacy Preserving Collaborative Filtering by Distributed MediationACM Transactions on Intelligent Systems and Technology10.1145/3542950Online publication date: 6-Jun-2022
https://doi.org/10.1145/3542950
Shmueli ETassa T(2020)Mediated Secure Multi-Party Protocols for Collaborative FilteringACM Transactions on Intelligent Systems and Technology10.1145/337540211:2(1-25)Online publication date: 24-Feb-2020
https://doi.org/10.1145/3375402
Jung JChun SJin XLee K(2018)Quantitative Computation of Social Strength in Social Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2018.28699335:5(4066-4075)Online publication date: Oct-2018
https://doi.org/10.1109/JIOT.2018.2869933
Barshap GTassa T(2018)Privacy-Preserving Planarity Testing of Distributed GraphsData and Applications Security and Privacy XXXII10.1007/978-3-319-95729-6_9(131-147)Online publication date: 10-Jul-2018
https://doi.org/10.1007/978-3-319-95729-6_9
Shmueli ETassa TCremonesi PRicci FBerkovsky STuzhilin A(2017)Secure Multi-Party Protocols for Item-Based Collaborative FilteringProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109881(89-97)Online publication date: 27-Aug-2017
https://dl.acm.org/doi/10.1145/3109859.3109881

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