Privacy or utility in data collection? A contract theoretic approach

IEEE Journal on Selected Topics in Signal Processing

Volumn 9, Issue 7, 2015, Pages 1256-1269

  Xu, Lei ^a   Jiang, Chunxiao ^a   Chen, Yan ^b   Ren, Yong ^a   Liu, K J Ray ^b  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

contract theory; data anonymization; data collecting; optimal control; Privacy preserving

Indexed keywords

BIG DATA; DATA ACQUISITION; DATA MINING; ECONOMIC AND SOCIAL EFFECTS;

CONTRACT THEORY; DATA ANONYMIZATION; DATA COLLECTING; OPTIMAL CONTROLS; PRIVACY PRESERVING;

DATA PRIVACY;

EID: 84942327005     PISSN: 19324553     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSTSP.2015.2425798     Document Type: Article

References (21)

1. B. Fung, K. Wang, R. Chen, and P. S. Yu. Privacy-preserving data publishing: A survey of recent developments, " ACM Comput. Surv., vol. 42, no. 4, pp. 1-53, 2010
2. R. Agrawal and R. Srikant. Privacy-preserving data mining, " SIGMOD Rec., vol. 29, no. 2, pp. 439-450, 2000
3. L. Sweeney. Achieving k-Anonymity privacy protection using generalization and suppression, " Int. J. Uncertainty, Fuzziness, Knowl.-Based Syst., vol. 10, no. 05, pp. 571-588, 2002
4. A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. L-diversity: Privacy beyond k-Anonymity, " in Proc. 22nd Int. Conf. Data Eng. (ICDE'06), Apr. 2006, pp. 24-24
5. N. Li, T. Li, and S. Venkatasubramanian. T-closeness: Privacy beyond k-Anonymity and l-diversity, " in Proc. ICDE, 2007, vol. 7, pp. 106-115
6. C. C. Aggarwal and S. Y. Philip, A General Survey of Privacy-Preserving Data Mining Models and Algorithms. New York, NY, USA: Springer, 2008
7. S. Matwin. Privacy-preserving data mining techniques: Survey and challenges, " in Discrimination and Privacy in the Information Society. New York, NY, USA: Springer, 2013, pp. 209-221
8. A. Acquisti. The economics of privacy: Theoretical and empirical aspects, " 2013 [Online] . Available: http: //cusp.nyu.edu/wp-content/uploads/2013/09/C03-Acquisti-chapter.pdf
9. A. Roth. Buying private data at auction: The sensitive surveyor's problem. SIGecom Exchanges, vol. 11, no. 1, pp. 1-8, 2012
10. A. Ghosh and A. Roth. Selling privacy at auction, " in Proc. 12th ACM Conf. Electron. Commerce, 2011, pp. 199-208
11. L. K. Fleischer and Y.-H. Lyu. Approximately optimal auctions for selling privacy when costs are correlated with data, " in Proc. 13th ACM Conf. Electron. Commerce ACM, 2012, pp. 568-585
12. K. Ligett and A. Roth. Take it or leave it: Running a survey when privacy comes at a cost, " in Internet and Network Economics. New York, NY, USA: Springer, 2012, pp. 378-391
13. K. Nissim, S. Vadhan, and D. Xiao. Redrawing the boundaries on purchasing data from privacy-sensitive individuals, " in Proc. 5th Conf. Innovat. Theoret. Comput. Sci., 2014, pp. 411-422
14. C. Dwork. Differential privacy, " in Automata, Languages and Programming. New York, NY, USA: Springer, 2006, pp. 1-12
15. J.-J. Laffont and D. Martimort, The Theory of Incentives: The Principal-Agent Model. Princeton, NJ, USA: Princeton Univ. Press, 2009
16. Y. Gao, Y. Chen, C.-Y. Wang, and K. J. R. Liu. Optimal contract design for ancillary services in vehicle-to-grid networks, " in Proc. IEEE 3rd Int. Conf. Smart Grid Commun. (SmartGridComm), Nov. 2012, pp. 79-84
17. Y. Gao, Y. Chen, C.-Y. Wang, and K. J. R. Liu. A contract-based approach for ancillary services in v2g networks: Optimality and learning, " in Proc. IEEE INFOCOM, Apr. 2013, pp. 1151-1159
18. D. Kirk, Optimal Control Theory: An Introduction, ser. Dover Books on Electrical Engineering. New York, NY, USA: Dover, 2012
19. K. Bache and M. Lichman. UCI Machine Learning Repository, " 2013 [Online] . Available: http: //archive.ics.uci.edu/ml
20. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Incognito: Efficient full-domain k-Anonymity, " in Proc. ACM SIGMOD Int. Conf.Manage. Data, New York, NY, USA, 2005, pp. 49-60, ser. SIGMOD'05, ACM
21. F. Kohlmayer, F. Prasser, C. Eckert, A. Kemper, and K. Kuhn. Flash: Efficient, stable and optimal k-Anonymity, " in Proc. Privacy, Security, Risk and Trust (PASSAT), Int. Conf. and 2012 Int. Conf. Social Comput. (SocialCom), Sep. 2012, pp. 708-717