References to k-anonymty Literature

k-anonymity


The ability to collect and diseminate person-specifc data increases daily. Given the sensitive nature of personal information, such as health or financial-related knowledge, it is necessary to construct techniques to protect personal privacy in shared datbases. To protect privacy, the computer science community has proposed many models of protected databases. One particular model that has received considerable attention from computer scientists is called k-anonymity. Under k-anonymity, each piece of disclosed data is equivalent to at least k-1 other pieces of disclosed data over a set of attributes that are deemed to be privacy sensitive. Below are some citations we consider to be important with respect to the theory and application of k-anonymity. (If you have an additional citation you deem essential to this collection, please let us know.)


Application Areas: anonymity, privacy, confidentiality, data mining, databases


Selected References: Models and Methods

  1. Aggarwal C. On k-Anonymity and the curse of dimensionality, In Proceedings of the 31st International Conference on Very Large Databases (VLDB). 2005.

  2. Aggarwal G, et. al. Anonymizing tables, In Proceedings of the International Conference on Database Theory. 2005: 246-258.

  3. Aggarwal G, et. al. Approximation algorithms for k-anonymity, Journal of Privacy Technology. 2005; 20051120001.

  4. Atzori M, Bonchi F, Giannotti F, and Pedreschi D. k-Anonymous Patterns, In Proceedings of the Principles and Practice of Knowledge Discovery in Databases (PKDD). 2005: 10-21.

  5. Bayardo R and Agrawal R. Data privacy through optimal k-anonymization, In Proceedings of the 21st IEEE International Conference on Data Engineering. 2005: 217--228.

  6. Bertino E, Ooi BC, Yang Y, and Deng RH. Privacy and Ownership Preserving of Outsourced Medical Data, In Proceedings of the 21st IEEE International Conference on Data Engineering. 2005: 521-532.

  7. Bettini C, Wang, XS, and Jajodia S. Protecting privacy against location-based personal identification, Jonker W. and Petkovic M, eds. LNCS 3674: Proceedings of the 2nd Workshop on Secure Data Management, held in conjunction with the International Conference on Very Large Databases (VLDB), Springer Verlag, Berlin, Germany. 2005: 185-199.

  8. Fung B, Wang K, and Yu P. Top-down specialization for information and privacy preservation, In Proceedings of the 21st IEEE International Conference on Data Engineering. 2005: 205-216.

  9. Domingo-Ferrer D and Torra V. Ordinal, continuous, and heterogeneous k-anonymity through microaggregation, Data Mining and Knowledge Discovery. 2005; 11(2): 195-212.

  10. Gedik B and Liu L. Location privacy in mobile systems: a personalized anonymization model, In Proceedings of the 25th International Conference on Distributed Computing Systems. 2005

  11. Gilburd B, Schuster A, and Wolff R. k-TTP: a new privacy model for large-scale distributed environments, In Proceedings of the ACM SIGKDD International Conference on Data Mining and Knowledge Discovery. 2004.

  12. Gross R, Airoldi E, Malin B, and Sweeney L. Integrating utility into face de-identification, In Proceedings of the 5th Privacy Enhancing Technologies Workshop. 2005.

  13. Gruteser M and Grunwald D. Anonymous usage of location-based services through spatial and temporal cloaking, In Proceedings of ACM/USENIX International Conference on Mobile Systems, Applications, and Services (Mobisys). 2003.

  14. Iyengar V. Transforming data to satisfy privacy constraints, In Proceedings of the ACM SIGKDD International Conference on Data Mining and Knowledge Discovery. 2002: 279-288.

  15. Jiang W and Clifton C. Privacy-Preserving Distributed k-Anonymity, In Proceedings of Data and Applications Security XIX, 19th Annual IFIP WG 11.3 Working Conference on Data and Applications Security (DBSEC). 2005: 166-177.

  16. LeFevre K, DeWitt D, and Ramakrishnan R. Incognito: efficient full-domain k-anonymity, In Proceedings of the ACM SIGMOD International Conference on Management of Data. 2005: 49-60.

  17. LeFevre K, DeWitt D, and Ramakrishnan R. Mondrian multidimensional k-anononymity, In Proceedings of 22nd IEEE International Conference on Data Engineering. 2006.

  18. Lin Z, Hewitt M, and Altman R. Using binning to maintain confidentiality of medical data, In Proceedings of the American Medical Informatics Association Annual Symposium. 2002: 454-458.

  19. Machanavajjhala A, Gehrke J, and Kifer D. l-diversity: privacy beyond k-anonymity, In Proceedings of the 22nd IEEE International Conference on Data Engineering. 2006.

  20. Malin B. Protecting genomic sequence anonymity with generalization lattices, Methods of Information in Medicine. 2005; 44(5): 687-692.

  21. Meyerson A and Williams R. On the complexity of optimal k-anonymity, In Proceedings of the ACM SIGMOD-SIGACT-SIGART Principles of Database Systems. 2004: 223-228.

  22. Newton E, Sweeney L, and Malin B. Preserving privacy by de-identifying facial images, IEEE Transactions on Knowledge and Data Engineering. 2005; 17(2): 232-243.

  23. Nergiz ME and Clifton C. Thoughts on k-anonymization, Second Intenational Workshop on Privacy Data Management (PDM'06). 2006: 96.

  24. Samarati P. Protecting respondents' identities in microdata release, IEEE Transactions on Knowledge and Data Engineering. 2001; 13(6): 1010-1027.

  25. Samarati P and Sweeney L. Generalizing data to provide anonymity when disclosing information (Abstract), In Proceedings of the 7th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. 1998: 188.

  26. Samarati P and Sweeney L. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression, Technical Report SRI-CSL-98-04, SRI Computer Science Laboratory. Palo Alto, CA. 1998.

  27. Sweeney L. Datafly: a system for providing anonymity in medical data, In Database Securty XI: Status and Prospects, IFIP TC11 WG11.3 11th International Conference on Database Security, Chapman & Hall. 1997: 356--381.

  28. Sweeney L. Guaranteeing anonymity when sharing medical data, the Datafly system, In Proceedings of the American Medical Informatics Association Annual Symposium. 1997: 51-55.

  29. Sweeney L. Computational disclosure control: theory and practice, PhD Thesis, Massachusetts Institute of Technology, Cambridge, MA. 2001.

  30. Sweeney L. k-anonymity: a model for protecting privacy, International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems. 2002; 10(5): 557-570.

  31. Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression, International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems. 2002; 10(5): 571-588.

  32. Trombetta A, Bertino E. Private Updates to Anonymous Databases, In Proceedings of the 22nd International Conference on Data Engineering. 2006: 116.

  33. von Ahn L, Bortz A, and Hopper NJ. k-anonymous message transmission, In Proceedings of the 10th ACM Conference on Computer and Communications Security. 2003.

  34. Vaidya J and Clifton C. Privacy-preserving top-k queries, In Proceedings of the 21st IEEE International Conference on Data Engineering. 2005.

  35. Wang K, Yu P, and Chakraborty S. Bottom-up generalization: A data mining solution to privacy protection, In Proceedings of the IEEE International Conference on Data Mining. 2004: 249-256.

  36. Winkler W. Using simulated annealing for k-anonymity, Research Report Series Number 2002-07, US Census Bureau Statistical Research Division, Washington, DC. 2002.

  37. Xu S and Yung M. k-anonymous secret handshakes with reusable credentials. Research Report Series Number 2002-07, US Census Bureau Statistical Research Division, Washington, DC. 2002.

  38. Wu X and BertinoE. Achieving K-anonymity in mobile ad hoc networks, In Proceedings of the 1st IEEE ICNP Workshop on Secure Network Protocols (NPSec). 2005: 37-42.

  39. Yao C., Wang S., and Jajodia S. Checking for k-Anonymity Violation by Views, In Proceedings of the 11th ACM Conference on Computer and Communications Security. 2004: 158-167.

  40. Zhong S., Yang Z., and Wright R. Privacy-enhancing k-anonymization of customer data, In Proceedings of the ACM SIGMOD-SIGACT-SIGART Principles of Database Systems, Baltimore, MD. 2005.


Related Links

This list was compiled in part by Bradley Malin. For additions or changes, please contact us.

Fall 2004 Data Privacy Lab