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Anonymity in Multi-Instance Micro-Data Publication

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 264)

Abstract

In this paper we study the problem of anonymity in multi-instance (MI) micro-data publication. The classical k-anonymity approach is shown to be insufficient and/or inappropriate for MI databases. Thus, it is extended to MI databases, resulting in a more general setting of MI k-anonymity. We show that MI k-anonymity problem is NP-Hard and the attack model for MI databases is different from that of single-instance databases. We make an observation that the introduced MI k-anonymity is not a strong privacy guarantee when anonymity sets are highly unbalanced with respect to instance counts. To this end a new anonymity principle, called p-certainty, which is unique to MI case is introduced. A clustering algorithms solving the p-certainty anonymity principle is developed and experimentally evaluated.

Keywords

  • Attack Model
  • Privacy Breach
  • Move Object Database
  • Feasible Mapping
  • Statistical Disclosure Control

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Abul O, Bonchi F, Nanni M (2008) Never walk alone: uncertainty for anonymity in moving objects databases. In: Proceedings of 24th IEEE international conference on data, engineering (ICDE’08)

    Google Scholar 

  2. Adam NR, Wortmann JC (1989) Security-control methods for statistical databases: a comparative study. ACM Comput Surv 21(4):515–556

    CrossRef  Google Scholar 

  3. Aggarwal G, Feder T, Kenthapadi K, Khuller S, Panigrahy R, Thomas D, Zhu A (2006) Achieving anonymity via clustering. In: Proceedings of 25rd ACM symposium on principles of database systems (PODS’06)

    Google Scholar 

  4. Aggarwal G, Feder T, Kenthapadi K, Motwani R, Panigrahy R, Thomas D, Zhu A (2005) Anonymizing tables. In: Proceedings of 10th international conference on database theory (ICDT’05)

    Google Scholar 

  5. Agrawal D, Aggarwal CC (2001) On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of 20th ACM symposium on principles of database systems (PODS’01), pp 247–255

    Google Scholar 

  6. Agrawal R, Srikant R (2000) Privacy-preserving data mining. In: Proceedings of 2000 ACM SIGMOD international conference on management of data (SIGMOD’00), pp 439–450

    Google Scholar 

  7. Domingo-Ferrer J, Mateo-Sanz JM (2002) Practical data-oriented microaggregation for statistical disclosure control. IEEE Trans Knowl Data Eng 14(1):189–201

    CrossRef  Google Scholar 

  8. Garey MR, Johson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. Freeman, New York

    Google Scholar 

  9. Kohavi R (1996) Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid. In: Proceedings of 2nd international conference on knowledge discovery and data mining (KDD’96)

    Google Scholar 

  10. Kriegel H-P, Pryakhin A, Schubert M (2006) An EM approach for clustering multi-instance objects. In: Proceedings of 10th Pacific-Asia conference on knowledge discovery and data mining (PAKDD’06)

    Google Scholar 

  11. Kwok JT, Cheung P-M (2007) Marginalized multi-instance kernels. In: Proceedings of 20th international joint conference on artificial intelligence (IJCAI’07)

    Google Scholar 

  12. LeFevre K, DeWitt DJ, Ramakrishnan R (2005) Incognito: efficient full-domain k-anonymity. In: Proceedings of 2005 ACM SIGMOD international conference on management of data (SIGMOD’05), pp 49–60

    Google Scholar 

  13. LeFevre K, DeWitt DJ, Ramakrishnan R (2006) Mondrian multidimensional k-anonymity. In: Proceedings of 22nd IEEE international conference on data, engineering (ICDE’06)

    Google Scholar 

  14. Li J, Wong RC-W, Fu AW-C, Pei J (2006) Achieving k-anonymity by clustering in attribute hierarchical structures. In: Proceedings of 8th international conference on data warehousing and knowledge, discovery (DaWaK’06)

    Google Scholar 

  15. Li N, Li T (2007) \(t\)-closeness: privacy beyond k-anonymity and l-diversity. In: Proceedings of 23rd IEEE international conference on data, engineering (ICDE’07)

    Google Scholar 

  16. Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M (2006) \(l\)-diversity: privacy beyond \(k\)-anonymity. In: Proceedings of 22nd IEEE international conference on data, engineering (ICDE’06)

    Google Scholar 

  17. Martin DJ, Kifer D, Machanavajjhala A, Gehrke J (2007) Worst-case background knowledge for privacy-preserving data publishing. In: Proceedings of 23rd IEEE international conference on data engineering (ICDE’07)

    Google Scholar 

  18. Meyerson A, Willliams R (2004) On the complexity of optimal k-anonymity. In: Proceedings of the 23rd ACM symposium on principles of database systems (PODS’04)

    Google Scholar 

  19. Nergiz M, Clifton C, Nergiz A (2007) Multirelational k-anonymity. In: Proceedings of data engineering, 2007. ICDE 2007, IEEE 23rd international conference on, pp 1417–1421

    Google Scholar 

  20. O’Leary DE (1991) Knowledge discovery as a threat to database security. In Piatetsky-Shapiro G, Frawley WJ (eds) Knowledge discovery in databases. AAAI/MIT Press, Cambridge, pp 507–516

    Google Scholar 

  21. Samarati P, Sweeney L (1998) Generalizing data to provide anonymity when disclosing information (abstract). In: Proceedings of 17th ACM symposium on principles of database systems (PODS’98)

    Google Scholar 

  22. Sweeney L (2002) k-anonymity: a model of protecting privacy. Int J Uncertainty Fuzziness Knowl Based Syst 10(5):557–570

    CrossRef  MATH  MathSciNet  Google Scholar 

  23. Wong R, Li J, Fu A, Wang K (2006) \((\alpha , k)\)-anonymity: an enhanced k-anonymity model for privacy-preserving data publishing. In: Proceedings of 12th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’06)

    Google Scholar 

  24. Xiao X, Tao Y (2007) m-invariance: towards privacy preserving re-publication of dynamic datasets. In: Proceedings of 2007 ACM SIGMOD international conference on management of data (SIGMOD’07)

    Google Scholar 

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Correspondence to Osman Abul .

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Abul, O. (2013). Anonymity in Multi-Instance Micro-Data Publication. In: Gelenbe, E., Lent, R. (eds) Information Sciences and Systems 2013. Lecture Notes in Electrical Engineering, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-01604-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-01604-7_32

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