Advertisement

\(\delta \)-privacy: Bounding Privacy Leaks in Privacy Preserving Data Mining

  • Zhizhou LiEmail author
  • Ten H. Lai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10436)

Abstract

We propose a new definition for privacy, called \(\delta \)-privacy, for privacy preserving data mining. The intuition of this work is, after obtaining a result from a data mining method, an adversary has better ability in discovering data providers’ privacy; if this improvement is large, the method, which generated the response, is not privacy considerate. \(\delta \)-privacy requires that no adversary could improve more than \(\delta \). This definition can be used to assess the risk of privacy leak in any data mining methods, in particular, we show its relations to differential privacy and data anonymity, the two major evaluation methods. We also provide a quantitative analysis on the tradeoff between privacy and utility, rigorously prove that the information gains of any \(\delta \)-private methods do not exceed \(\delta \). Under the framework of \(\delta \)-privacy, it is able to design a pricing mechanism for privacy-utility trading system, which is one of our major future works.

References

  1. 1.
    Agrawal, R., Srikant, R.: Privacy-preserving data mining. SIGMOD Rec. 29(2), 439–450 (2000). http://doi.acm.org/10.1145/335191.335438CrossRefGoogle Scholar
  2. 2.
    Brenner, H., Nissim, K.: Impossibility of differentially private universally optimal mechanisms. In: FOCS, pp. 71–80. IEEE Computer Society (2010)Google Scholar
  3. 3.
    Brickell, J., Shmatikov, V.: The cost of privacy: destruction of data-mining utility in anonymized data publishing. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 70–78. ACM, New York (2008)Google Scholar
  4. 4.
    Cormode, G., Procopiuc, C., Shen, E., Srivastava, D., Yu, T.: Empirical privacy and empirical utility of anonymized data. In: 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), pp. 77–82, April 2013Google Scholar
  5. 5.
    Delfs, H., Knebl, H.: Introduction to Cryptography - Principles and Applications. Information Security and Cryptography. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-79228-4_1CrossRefzbMATHGoogle Scholar
  7. 7.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). doi: 10.1007/11681878_14CrossRefGoogle Scholar
  8. 8.
    Dwork, C., Pottenger, R.: Toward practicing privacy. J. Am. Med. Inform. Assoc. 20(1), 102–108 (2013). http://jamia.bmj.com/content/20/1/102.abstractCrossRefGoogle Scholar
  9. 9.
    Ganta, S.R., Kasiviswanathan, S.P., Smith, A.: Composition attacks and auxiliary information in data privacy. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 265–273. ACM, NY, USA (2008). http://doi.acm.org/10.1145/1401890.1401926
  10. 10.
    Ghosh, A., Roughgarden, T., Sundararajan, M.: Universally utility-maximizing privacy mechanisms. In: Proceedings of the Forty-first Annual ACM Symposium on Theory of Computing, STOC 2009, pp. 351–360. ACM, NY, USA (2009). http://doi.acm.org/10.1145/1536414.1536464
  11. 11.
    Gupte, M., Sundararajan, M.: Universally optimal privacy mechanisms for minimax agents. In: Proceedings of the Twenty-ninth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2010, pp. 135–146. ACM, NY, USA (2010). http://doi.acm.org/10.1145/1807085.1807105
  12. 12.
    Katz, J., Lindell, Y.: Introduction to Modern Cryptography. Chapman & Hall/Crc Cryptography and Network Security Series. Chapman & Hall/CRC, Boca Raton (2007)zbMATHGoogle Scholar
  13. 13.
    Li, N., Li, T.: t-closeness: Privacy beyond k-anonymity and -diversity. In: Proceedings of IEEE 23rd International Conference on Data Engineering (ICDE 2007) (2007)Google Scholar
  14. 14.
    Li, T., Li, N.: On the tradeoff between privacy and utility in data publishing. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09, pp. 517–526. ACM, NY, USA (2009). http://doi.acm.org/10.1145/1557019.1557079
  15. 15.
    Lin, J.: Divergence measures based on the shannon entropy. IEEE Trans. Inform. Theory 37(1), 145–151 (1991)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000). doi: 10.1007/3-540-44598-6_3CrossRefGoogle Scholar
  17. 17.
    Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. In: Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, p. 24 (2006)Google Scholar
  18. 18.
    McSherry, F., Mironov, I.: Differentially private recommender systems: Building privacy into the net. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 627–636. ACM, NY, USA (2009). http://doi.acm.org/10.1145/1557019.1557090
  19. 19.
    McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD 2009, pp. 19–30. ACM, NY, USA (2009). http://doi.acm.org/10.1145/1559845.1559850
  20. 20.
    Parra-Arnau, J., Rebollo-Monedero, D., Forn, J.: Measuring the privacy of user profiles in personalized information systems. Future Gener. Comput. Syst. 33, 53–63 (2014). http://www.sciencedirect.com/science/article/pii/S0167739X1300006X, special Section on Applications of Intelligent Data and Knowledge Processing Technologies; Guest Editor: Dominik lzakCrossRefGoogle Scholar
  21. 21.
    Peters, F., Menzies, T., Gong, L., Zhang, H.: Balancing privacy and utility in cross-company defect prediction. IEEE Trans. Softw. Eng. 39(8), 1054–1068 (2013)CrossRefGoogle Scholar
  22. 22.
    Rebollo-Monedero, D., Parra-Arnau, J., Diaz, C., Forn, J.: On the measurement of privacy as an attackers estimation error. Int. J. Inf. Secur. 12(2), 129–149 (2013). http://dx.doi.org/10.1007/s10207-012-0182-5CrossRefGoogle Scholar
  23. 23.
    Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002). http://dx.doi.org/10.1142/S0218488502001648MathSciNetCrossRefGoogle Scholar
  24. 24.
    Venkatasubramanian, S.: Measures of anonymity. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining. ADBS, vol. 34. Springer, Boston (2008). doi: 10.1007/978-0-387-70992-5_4CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The Voleon GroupBerkeleyUSA
  2. 2.The Ohio State UniversityColumbusUSA

Personalised recommendations