Algorithm-safe privacy-preserving data publishing

Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings

Volumn , Issue , 2010, Pages 633-644

  Jin, Xin ^a   Zhang, Nan ^a   Das, Gautam ^b  

^a GEORGE WASHINGTON UNIVERSITY   (United States)

^b UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PUBLISHING; DATA UTILITIES; MONDRIAN; PRIVACY PRESERVING; TOOLSETS;

DATA PRIVACY; DATABASE SYSTEMS; PUBLISHING; TECHNOLOGY;

ALGORITHMS;

EID: 77952284440     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1739041.1739116     Document Type: Conference Paper

References (42)

1. G. Aggarwal, T. Feder, K. Kenthapadi, S. Khuller, R. Panigrahy, D. Thomas, and A. Zhu. Achieving anonymity via clustering. In PODS, pages 153-162, 2006.
2. G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu. Anonymizing tables. In ICDT, pages 246-258, 2005.
3. R. J. Bayardo and R. Agrawal. Data privacy through optimal k-anonymization. In ICDE, pages 217-228, 2005.
4. B. Chen, R. Ramakrishnan, and K. LeFevre. Privacy skyline: Privacy with multidimensional adversarial knowledge. In VLDB, pages 770-781, 2007.
5. T. M. Cover and J. A. Thomas. Elements of Information Theory. Wiley-Interscience, 1991.
6. W. Du, Z. Teng, and Z. Zhu. Privacy-maxent: Integrating background knowledge in privacy quantification. In SIGMOD, pages 459-472, 2008.
7. C. Dwork. Differntial privacy. In ICALP, pages 1-12, 2006.
8. B. C. M. Fung, K. Wang, and P. S. Yu. Top-down specialization for information and privacy preservation. In ICDE, pages 205-216, 2005.
9. G. Ghinita, P. Karras, P. Kalnis, and N. Mamoulis. Fast data anonymization with low information loss. In VLDB, pages 758-769, 2007.
10. G. Ghinita, Y. Tao, and P. Kalnis. On the anonymization of sparse high-dimensional data. In ICDE, pages 715-724, 2008.
11. T. Iwuchukwu and J. Naughton. k-anonymization as spatial indexing: Toward scalable and incremental anonymization. In VLDB, pages 746-757, 2007.
12. V. S. Iyengar. Transforming data to satisfy privacy constraints. In KDD, pages 279-288, 2002.
13. A. Kerckhoffs. La cryptographie militaire (military cryptography). Journal des sciences militaires, IX:5-83, 161-191, 1883.
14. D. Kifer and J. Gehrke. Injecting uility into anonymized datasets. In SIGMOD, pages 217-228, 2006.
15. N. Koudas, D. Srivastava, T. Yu, and Q. Zhang. Distribution-based microdata anonymization. In VLDB, 2009.
16. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Incognito: efficient full-domain k-anonymity. In SIGMOD, pages 49-60, 2005.
17. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Mondrian multidimensional k-anonymity. In ICDE, pages 25-35, 2006.
18. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Workload-aware anonymization. In KDD, pages 277-286, 2006.
19. J. Li, Y. Tao, and X. Xiao. Preservation of proximity privacy in publishing numeric sensitive data. In SIGMOD, pages 473-486, 2008.
20. N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and ℓ-diversity. In ICDE, pages 106-115, 2007.
21. T. Li and N. Li. Injector: Mining background knowledge for data anonymization. In ICDE, pages 446-455, 2008.
22. T. Li, N. Li, and J. Zhang. Modeling and integrating background knowledge in data anonymization. In ICDE, 2009.
23. K. Liu and E. Terzi. Towards identity anonymization on graphs. In SIGMOD, 2009.
24. C.-Y. C. M. F. Mokbel and W. G. Aref. The new casper: Query procesing for location services without compromising privacy. In VLDB, 2006.
25. A. Machanavajjhala, J. Gehrke, and M. Goetz. Data publishing against realistic adversaries. In VLDB, 2009.
26. A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. ℓ-diversity: Privacy beyond k-anonymity. 2006.
27. D. J. Martin, D. Kifer, A. Machanavajjhala, J. Gehrke, and J. Halpern. Worst-case background knowledge for privacy-preserving data publishing. In ICDE, pages 126-135, 2007.
28. A. Meyerson and R. Williams. On the complexity of optimal k-anonymity. In PODS, pages 223-228, 2004.
29. M. E. Nergiz, M. Atzori, and C. Clifton. Hiding the presence of individuals from shared databases. In SIGMOD, pages 665-676, 2007.
30. H. Park and K. Shim. Approximate algorithms for k-anonymity. In SIGMOD, pages 67-78, 2007.
31. V. Rastogi, S. Hong, and D. Suciu. The boundary between privacy and utility in data publishing. In VLDB, pages 531-542, 2007.
32. P. Samarati and L. Sweeney. Protecting privacy when disclosing information: k-anonymity and its enforcement through generlization and suppression. Technical report, CMU, SRI, 1998.
33. Y. Tao, X. Xiao, J. Li, and D. Zhang. On anti-corruption privacy preserving publication. In ICDE, pages 725-734, 2008.
34. R. C. Wong, A. W. Fu, K. Wang, and J. Pei. Minimality attack in privacy-preserving data publishing. In VLDB, pages 543-554, 2007.
35. R. C. Wong, J. Li, A. W. Fu, and K. Wang. (α, k)-anonymity: An enhanced k-anonymity model for privacy-preserving data publishing. In KDD, pages 754-759, 2006.
36. X. Xiao and Y. Tao. Anatomy: Simple and effective privacy preservation. In VLDB, pages 139-150, 2006.
37. X. Xiao and Y. Tao. Personalized privacy preservation. In SIGMOD, pages 229-240, 2006.
38. X. Xiao and Y. Tao. m-invariance: towards privacy preserving re-publication of dynamic datasets. In SIGMOD, pages 689-700, 2007.
39. J. Xu, W. Wang, J. Pei, X. Wang, B. Shi, and A. W. Fu. Utility-based anonymization using local recoding. In KDD, pages 785-790, 2006.
40. H. Yeye and J. Naughton. Anonymization of set-valued data via top-down, local generalization. In Advances in Cryptography: Proceedings of Eurocrypt 2004, 2009.
41. L. Zhang, S. Jajodia, and A. Brodsky. Information disclosure under realistic assumptions: Privacy versus optimality. In CCS, 2007.
42. Q. Zhang, N. Koudas, D. Srivastava, and T. Yu. Aggregate query answering on anonymized tables. In ICDE, pages 116-125, 2007.