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Relationship privacy: output perturbation for queries with joins

Published:29 June 2009Publication 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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. H. W. Block, T. H. Savits, and M. Shaked. Some concepts of negative dependence. In Ann. of Prob., 1982.Google ScholarGoogle ScholarCross RefCross Ref
  3. A. Campan and T. M. Truta. A clustering approach for data and structural anonymity in social networks. In PinKDD, 2008.Google ScholarGoogle Scholar
  4. C. Dwork. Differential privacy. In ICALP, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In TCC, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. Evfimievski, J. Gehrke, and R. Srikant. Limiting privacy breaches in privacy preserving data mining. In PODS, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. V. Evfimievski, R. Fagin, and D. P. Woodruff. Epistemic privacy. In PODS, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. T. Feder and M. Mihail. Balanced matroids. In STOC, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Full version: http://www.cs.washington.edu/homes/vibhor/relationship_privacy.pdf.Google ScholarGoogle Scholar
  10. S. Ganta, S. Kasiviswanathan, and A. Smith. Composition attacks and auxiliary information in data privacy. In KDD, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. Hay, G. Miklau, D. Jensen, D. Towsley, and P. Weis. Resisting structural re-identification in anonymized social networks. In VLDB, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. K. Liu and E. Terzi. Towards identity anonymization on graphs. In SIGMOD, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. In ICDE, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. G. Miklau and D. Suciu. A formal analysis of information disclosure in data exchange. In SIGMOD, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. M. Newman. The structure and function of complex networks. SIREV: SIAM Review, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  16. K. Nissim, S. Raskhodnikova, and A. Smith. Smooth sensitivity and sampling in private data analysis. In STOC, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. V. Rastogi, D. Suciu, and S. Hong. The boundary between privacy and utility in data publishing. In VLDB, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. G. Shanthikumar and H.-W. Koo. On uniform conditional stochastic order conditioned on planar regions. In Ann. of Probab., 1990.Google ScholarGoogle Scholar
  19. V. Vu. Concentration of non-lipschitz functions and applications. RSA, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. X. Ying and X. Wu. Randomizing social networks: a spectrum preserving approach. In SIAM, 2007.Google ScholarGoogle Scholar
  21. E. Zheleva and L. Getoor. Preserving the privacy of sensitive relationships in graph data. In PinKDD, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. B. Zhou and J. Pei. Preserving privacy in social networks against neighborhood attacks. In ICDE, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library

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