Preservation of proximity privacy in publishing numerical sensitive data

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2008, Pages 473-485

  Li, Jiexing ^a   Tao, Yufei ^a   Xiao, Kui ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

( , m) anonymity; Anonymization; Numeric; Privacy

Indexed keywords

ANALYTICAL STUDIES; ANONYMIZATION; GENERALIZATION ALGORITHMS; HIGH CONFIDENCES; NUMERIC; PRIVACY; PRIVACY THREATS; REAL DATUMS; SENSITIVE ATTRIBUTES; SENSITIVE DATUMS;

PUBLISHING;

EID: 57149132810     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1376616.1376666     Document Type: Conference Paper

References (44)

1. C. C. Aggarwal and P. S. Yu. A condensation approach to privacy preserving data mining. In Proc. of Extending Database Technology (EDBT), pages 183-199, 2004.
2. G. Aggarwal, T. Feder, K. Kenthapadi, S. Khuller, R. Panigrahy, D. Thomas, and A. Zhu. Achieving anonymity via clustering. In Proc. of ACM Symposium on Principles of Database Systems (PODS), pages 153-162, 2006.
3. G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy. D. Thomas, and A. Zhu. Anonymizing tables. In Proc. of International Conference on Database Theory (ICDT), pages 246-258, 2005.
4. R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu. Hippocratic databases. In Proc. of Very Large Data Bases (VLDB), pages 143-154, 2002.
5. R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proc. of ACM Management of Data (SIGMOD), pages 439-450, 2000.
6. R. Agrawal, R. Srikant, and D. Thomas. Privacy preserving olap. In Proc. of ACM Management of Data (SIGMOD), pages 251-262, 2005.
7. R. Bayardo and R. Agrawal. Data privacy through optimal k-anonymization. In Proc. of International Conference on Data Engineering (ICDE), pages 217-228, 2005.
8. E. Bertino, C. Bettini, E. Ferrari, and P. Samarati. An access control model supporting periodicity constraints and temporal reasoning. A CM Transactions on Database Systems (TODS), 23(3):231-285, 1998.
9. E. Bertino and E. Ferrari. Secure and selective dissemination of xml documents. ACM Trans. Inf. Syst. Secur., 5(3):290-331, 2002.
10. A. Blum, C. Dwork, F. McSherry, and K. Nissim. Practical privacy: the sulq framework. In Proc. of ACM Symposium on Principles of Database Systems (PODS), pages 128-138, 2005.
11. J.-W. Byun, Y. Sohn, E. Bertino, and N. Li. Secure anonymization for incremental datasets. In Secure Data Management (SDM), pages 48-63, 2006.
12. B.-C. Chen, R. Ramakrishnan, and K. LeFevre. Privacy skyline: Privacy with multidimensional adversarial knowledge. In Proc. of Very Large Data Bases (VLDB), pages 770-781, 2007.
13. Y. Du, T. Xia, Y. Tao, D. Zhang, and F. Zhu. On multidimensional k-anonymity with local recoding generalization. In Proc. of International Conference on Data Engineering (ICDE), pages 1422-1424, 2007.
14. C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography Conference (TCC), pages 265-284, 2006.
15. A. V. Evfimievski, J. Gehrke, and R. Srikant. Limiting privacy breaches in privacy preserving data mining. In Proc. of ACM Symposium, on Principles of Database Systems (PODS), pages 211-222, 2003.
16. R. Floyd and R. Rivest. Expected time bounds for selection. In Communications of the ACM (CACM), volume 18, pages 165-172, 1975.
17. B. C. M. Fung, K. Wang, and P. S. Yu. Top-down specialization for information and privacy preservation. In Proc. of International Conference on Data Engineering (ICDE), pages 205-216, 2005.
18. G. Ghinita, P. Karras, P. Kalnis, and N. Mamoulis. Fast data anonymization with low information loss. In Proc. of Very Large Data Bases (VLDB), pages 758-769, 2007.
19. T. Iwuchukwu and J. F. Naughton. K-anonymization as spatial indexing: Toward scalable and incremental anonymization. In Proc. of Very Large Data Bases (VLDB), pages 746-757, 2007.
20. V. Iyengar. Transforming data to satisfy privacy constraints. In Proc. of ACM Knowledge Discovery and Data Mining (SIGKDD), pages 279-288, 2002.
21. W. Jiang and C. Clifton. A secure distributed framework for achieving k-anonymity. The VLDB Journal, 15(4):316-333, 2006.
22. D. Kifer and J. Gehrke. Injecting utility into anonymized datasets. In Proc. of ACM Management of Data (SIGMOD), pages 217-228, 2006.
23. K. LeFevre, D. DeWitt, and R. Ramakrishnan. Workload-aware anonymization. In Proc. of ACM Knowledge Discovery and Data Mining (SIGKDD), 2006.
24. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Incognito: Efficient full-domain k-anonymity. In Proc. of ACM Management of Data (SIGMOD), pages 49-60, 2005.
25. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Mondrian multidimensional k-anonymity. In Proc. of International Conference on Data Engineering (ICDE), pages 277-286, 2006.
26. N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and 1-diversity. In Proc. of International Conference on Data Engineering (ICDE), pages 106-115, 2007.
27. A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. 1-diversity: Privacy beyond k-anonymity. In Proc. of International Conference on Data Engineering (ICDE), page 24, 2006.
28. D. Martin, D. Kifer, A. Machanavajjhala, J. Gehrke, and J. Halpern. Worst-case background knowledge in privacy. In Proc. of International Conference on Data Engineering (ICDE), 2007.
29. A. Meyerson and R. Williams. On the complexity of optimal k-anonymity. In Proc. of ACM Symposium on Principles of Database Systems (PODS), pages 223-228, 2004.
30. S. U. Nabar, B. Marthi, K. Kenthapadi, N. Mishra, and R. Motwani. Towards robustness in query auditing. In Proc. of Very Large Data Bases (VLDB), pages 151-162, 2006.
31. M. E. Nergiz, M. Atzori, and C. Clifton. Hiding the presence of individuals from shared databases. In Proc. of ACM Management of Data (SIGMOD), pages 665-676, 2007.
32. H. Park and K. Shim. Approximate algorithms for k-anonymity. In Proc. of A CM Management of Data (SIGMOD), pages 67-78, 2007.
33. V. Rastogi, S. Hong, and D. Suciu. The boundary between privacy and utilhy in data publishing. In Proc. of Very Large Data Bases (VLDB), pages 531-542, 2007.
34. J. Rothe. Some facets of complexity theory and cryptography: A five-lecture tutorial. ACM Computing Surveys, 34(4):504-549, 2002.
35. P. Samarati. Protecting respondents' identities in microdata release. IEEE Transactions on Knowledge and Data Engineering (TKDE), 13(6): 1010-1027, 2001.
36. L. Sweeney, k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness, and, Knowlege-Based Systems, 10(5):557-570, 2002.
37. J. Vaidya and C. Clifton. Privacy-preserving k-means clustering over vertically partitioned data. In Proc. of ACM Knowledge Discovery and Data Alining (SIGKDD), pages 206-215, 2003.
38. K. Wang and B. C. M. Fung. Anonymizing sequential releases. In Proc. of ACM Knowledge Discovery and Data Mining (SIGKDD), pages 414-423, 2006.
39. R. C.-W. Wong, A. W.-C. Fu, K. Wang, and J. Pei. Minimality attack in privacy preserving data publishing. In Proc. of Very Large Data Bases (VLDB), pages 543-554, 2007.
40. R. C.-W. Wong, J. Li, A. W.-C. Fu, and K. Wang, (alpha, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In Proc. of ACM Knowledge Discovery and Data Alining (SIGKDD), pages 754-759, 2006.
41. X. Xiao and Y. Tao. Anatomy: Simple and effective privacy preservation. In Proc. of Very Large Data Bases (VLDB), pages 139-150, 2006.
42. X. Xiao and Y. Tao. m-invariance: towards privacy preserving re-publication of dynamic datasets. In Proc. of ACM Management of Data (SIGMOD), pages 689-700, 2007.
43. J. Xu, W. Wang, J. Pei, X. Wang, B. Shi, and A. W.-C. Fu. Utility-based anonymization using local recoding. In Proc. of ACM Knowledge Discovery and Data Mining (SIGKDD), pages 785-790, 2006.
44. Q. Zhang, N. Koudas, D. Srivastava, and T. Yu. Aggregate query answering on anonymized tables. In Proc. of International Conference on Data Engineering (ICDE), pages 116-125, 2007.