Privacy-preserving data sharing in cloud computing

Journal of Computer Science and Technology

Volumn 25, Issue 3, 2010, Pages 401-414

  Wang, Hui ^a  

^a Stevens Institute of Technology   (United States)

Author keywords

Anonymity; Cloud computing; Data sharing; Privacy; Utility

Indexed keywords

CLOUD COMPUTING;

ANONYMITY; DATA SHARING; EFFECTIVENESS AND EFFICIENCIES; INDIVIDUAL PRIVACY; INFORMATION LOSS; PRIVACY PRESERVING; SENSITIVE INFORMATIONS; UTILITY;

DATA PRIVACY;

EID: 77955863828     PISSN: 10009000     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11390-010-9333-1     Document Type: Article

References (24)

1. Weiss A. Computing in the clouds. Net Worker, Dec. 2007, 11 (4):16-25.
2. Hayes B. Cloud computing. Communications of the ACM, 2008, 51 (7):9-11.
3. Nergiz M E, Atzori M, Clifton C W. Hiding the presence of individuals from shared databases. In Proc. ACM's Special Interest Group on Management of Data (SIGMOD 2007), Beijing, China, June 11-17, 2007, pp. 665-676.
4. Samarati P, Sweeney L. Generalizing data to provide anonymity when disclosing information. In Proc. ACM International Conference on Principles of Database Systems (PODS), Seattle, USA, June 1-4, 1998, p. 188.
5. Samarati P, Sweeney L. Protecting privacy when disclosing information: κ-anonymity and its enforcement through generalization and suppression. Technical Report, SRI International, 1998.
6. Sweeney L. κ-anonymity: A model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 2002, 10 (5):557-570.
7. Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M. L-diversity: Privacy beyond κ-anonymity. In Proc. International Conference on Data Engineering Conference (ICDE), Atlanta, USA, Apr. 2006, p. 24.
8. Xiao X, Tao Y. Anatomy: Simple and effective privacy preservation. In Proc. Very Large Data Base Conference (VLDB), Seoul, Korea, Sept. 12-15, 2006, pp. 139-150.
9. Zhang Q, Koudas N, Srivastava D, Yu T. Aggregate query answering on anonymized tables. In Proc. International Conference on Data Engineering Conference (ICDE), Istanbul, Turkey, Apr. 15-20, 2007, pp. 116-125.
10. Ghinita G, Karras P, Kalnis P, Mamoulis N. Fast data anonymization with low information loss. In Proc. Very Large Data Base Conference (VLDB), Vienna, Austria, Sept. 23-27, 2007, pp. 758-769.
11. Li N, Li T. t-Closeness: Privacy beyond K-anonymity and l-diversity. In Proc. International Conference on Data Engineering Conference (ICDE), Istanbul, Turkey, April 15-20, 2007, pp. 106-115.
12. Wong R C W, Li J, Fu A W C, Wang K. (α, κ)-anonymity: An enhanced κ-anonymity model for privacy-preserving data publishing. In Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), Philadelphia, USA, Aug. 20-23, 2006, pp. 754-759.
13. Bayardo R J, Agrawal R. Data privacy through optimal κ- anonymization. In Proc. International Conference on Data Engineering Conference (ICDE), Tokyo, Japan, Apr. 5-8, 2005, pp. 217-228.
14. Meyerson A, Williams R. On the complexity of optimal κ-anonymity. In Proc. ACM International Conference on Principles of Database Systems (PODS), Paris, France, June 14-16, 2004, pp. 223-228.
15. Aggarwal C C. On κ-anonymity and the curse of dimensionality. In Proc. Very Large Data Base Conference (VLDB), Trondheim, Norway, June 14-16, 2005, pp. 901-909.
16. Martin D J, Kifer D, Machanavajjhala A, Gehrke J, Halpern J Y. Worst-case background knowledge for privacy-preserving data publishing. In Proc. International Conference on Data Engineering Conference (ICDE), Istanbul, Turkey, April 15-20, 2007, pp. 126-135.
17. Fung B C M, Wang K, Chen R, Yu P S. Privacy-preserving data publishing: A survey on recent developments. ACM Computing Surveys (CSUR), December 2010, 42 (4). (to appear).
18. Rastogi V, Suciu D, Hong S. The boundary between privacy and utility in data publishing. In Proc. Very Large Data Base Conference (VLDB), Vienna, Austria, Sept. 23-27, 2007, pp. 531-542.
19. Samarati P. Protecting respondents' identities in microdata release. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2001, 13 (6):1010-1027.
20. Iyengar V S. Transforming data to satisfy privacy constraints. In Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), Edmonton, Canada, July 23-26, 2002, pp. 279-288.
21. Xu J, Wang W, Pei J, Wang X, Shi B, Fu A W C. Utilitybased anonymization using local recoding. In Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), Philadelphia, USA, Aug. 20-23, 2006, pp. 785-790.
22. Kifer D, Gehrke J. Injecting utility into anonymized datasets. In Proc. ACM's Special Interest Group on Management Of Data (SIGMOD), Chicago, USA, June 27-29, 2006, pp. 217-228.
23. LeFevre K, DeWitt D, Ramakrishnan R. Incognito: Efficient full-domain κ-anonymity. In Proc. ACM's Special Interest Group on Management Of Data (SIGMOD), Baltimore, USA, June 14-16, 2005, pp. 49-60.
24. LeFevre K, DeWitt D, Ramakrishnan R. Mondrian multidimensional κ-anonymity. In Proc. International Conference on Data Engineering Conference (ICDE), Tokyo, Japan, Apr. 5-8, 2005, p. 25.