Incognito: efficient full-domain K-anonymity

ABSTRACT

A number of organizations publish microdata for purposes such as public health and demographic research. Although attributes that clearly identify individuals, such as Name and Social Security Number, are generally removed, these databases can sometimes be joined with other public databases on attributes such as Zipcode, Sex, and Birthdate to re-identify individuals who were supposed to remain anonymous. "Joining" attacks are made easier by the availability of other, complementary, databases over the Internet.K-anonymization is a technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In this paper, we provide a practical framework for implementing one model of k-anonymization, called full-domain generalization. We introduce a set of algorithms for producing minimal full-domain generalizations, and show that these algorithms perform up to an order of magnitude faster than previous algorithms on two real-life databases.Besides full-domain generalization, numerous other models have also been proposed for k-anonymization. The second contribution in this paper is a single taxonomy that categorizes previous models and introduces some promising new alternatives.

REFERENCES

• 1: G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu. Anonymizing tables. In Proc. of the 10th Int'l Conference on Database Theory, January 2005.
• 2: Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
• 3: Roberto J. Bayardo , Rakesh Agrawal, Data Privacy through Optimal k-Anonymization, Proceedings of the 21st International Conference on Data Engineering (ICDE'05), p.217-228, April 05-08, 2005 [doi>10.1109/ICDE.2005.42]
• 4: Richard Ernest Bellman, Dynamic Programming, Dover Publications, Incorporated, 2003
• 5: C. Blake and C. Merz. UCI repository of machine learning databases, 1998.
• 6: Surajit Chaudhuri , Umeshwar Dayal, An overview of data warehousing and OLAP technology, ACM SIGMOD Record, v.26 n.1, p.65-74, March 1997 [doi>10.1145/248603.248616]
• 7: Benjamin C. M. Fung , Ke Wang , Philip S. Yu, Top-Down Specialization for Information and Privacy Preservation, Proceedings of the 21st International Conference on Data Engineering (ICDE'05), p.205-216, April 05-08, 2005 [doi>10.1109/ICDE.2005.143]
• 8: Jim Gray , Surajit Chaudhuri , Adam Bosworth , Andrew Layman , Don Reichart , Murali Venkatrao , Frank Pellow , Hamid Pirahesh, Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals, Data Mining and Knowledge Discovery, v.1 n.1, p.29-53, 1997 [doi>10.1023/A:1009726021843]
• 9: Venky Harinarayan , Anand Rajaraman , Jeffrey D. Ullman, Implementing data cubes efficiently, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.205-216, June 04-06, 1996, Montreal, Quebec, Canada
• 10: A. Hundepool and L. Willenborg. μ- and μ-ARGUS: Software for statistical disclosure control. In Proc. of the Third Int'l Seminar on Statistical Confidentiality, 1996.
• 11: Vijay S. Iyengar, Transforming data to satisfy privacy constraints, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775089]
• 12: K. LeFevre, D. DeWitt, and R. Ramakrishnan. Multidimensional k-anonymity. Technical Report 1521, University of Wisconsin, 2005.
• 13: Adam Meyerson , Ryan Williams, On the complexity of optimal K-anonymity, Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, June 14-16, 2004, Paris, France [doi>10.1145/1055558.1055591]
• 14: P. Samarati, Protecting Respondents' Identities in Microdata Release, IEEE Transactions on Knowledge and Data Engineering, v.13 n.6, p.1010-1027, November 2001 [doi>10.1109/69.971193]
• 15: P. Samarati and L. Sweeney. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technical Report SRI-CSL-98-04, SRI Computer Science Laboratory, 1998.
• 16: Ramakrishnan Srikant , Rakesh Agrawal, Mining Generalized Association Rules, Proceedings of the 21th International Conference on Very Large Data Bases, p.407-419, September 11-15, 1995
• 17: Latanya Sweeney, Achieving k-anonymity privacy protection using generalization and suppression, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, v.10 n.5, p.571-588, October 2002 [doi>10.1142/S021848850200165X]
• 18: Latanya Sweeney, k-anonymity: a model for protecting privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, v.10 n.5, p.557-570, October 2002 [doi>10.1142/S0218488502001648]
• 19: Ke Wang , Philip S. Yu , Sourav Chakraborty, Bottom-Up Generalization: A Data Mining Solution to Privacy Protection, Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04), p.249-256, November 01-04, 2004
• 20: L. Willenborg and T. deWaal. Elements of Statistical Disclosure Control. Springer Verlag Lecture Notes in Statistics, 2000.
• 21: W. Winkler. Using simulated annealing for k-anonymity. Research Report 2002-07, US Census Bureau Statistical Research Division, November 2002.