Abstract
In a recent paper Dinur and Nissim considered a statistical database in which a trusted database administrator monitors queries and introduces noise to the responses with the goal of maintaining data privacy [5]. Under a rigorous definition of breach of privacy, Dinur and Nissim proved that unless the total number of queries is sub-linear in the size of the database, a substantial amount of noise is required to avoid a breach, rendering the database almost useless.
As databases grow increasingly large, the possibility of being able to query only a sub-linear number of times becomes realistic. We further investigate this situation, generalizing the previous work in two important directions: multi-attribute databases (previous work dealt only with single-attribute databases) and vertically partitioned databases, in which different subsets of attributes are stored in different databases. In addition, we show how to use our techniques for datamining on published noisy statistics.
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References
Agrawal, D., Aggarwal, C.: On the Design and Quantification of Privacy Preserving Data Mining Algorithms. In: Proceedings of the 20th Symposium on Principles of Database Systems (2001)
Adam, N.R., Wortmann, J.C.: Security-Control Methods for Statistical Databases: A Comparative Study. ACM Computing Surveys 21(4), 515–556 (1989)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proc. of the ACM SIGMOD Conference on Management of Data, pp. 439–450 (2000)
Chawla, S., Dwork, C., McSherry, F., Smith, A., Wee, H.: Toward Privacy in Public Databases (submitted for publication) (2004)
Dinur, I., Nissim, K.: Revealing information while preserving privacy. In: Proceedings of the Twenty-Second ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 202–210 (2003)
Duncan, G.: Confidentiality and statistical disclosure limitation. In: Smelser, N., Baltes, P. (eds.) International Encyclopedia of the Social and Behavioral Sciences, Elsevier, New York (2001)
Evfimievski, A.V., Gehrke, J., Srikant, R.: Limiting privacy breaches in privacy preserving data mining. In: Proceedings of the Twenty-Second ACM SIGACTSIGMOD- SIGART Symposium on Principles of Database Systems, pp. 211–222 (2003)
Fienberg, S.: Confidentiality and Data Protection Through Disclosure Limitation: Evolving Principles and Technical Advances. In: IAOS Conference on Statistics, Development and Human Rights (September 2000), available at http://www.statistik.admin.ch/about/international/fienberg_final_paper.doc
Fienberg, S., Makov, U., Steele, R.: Disclosure Limitation and Related Methods for Categorical Data. Journal of Official Statistics 14, 485–502 (1998)
Franconi, L., Merola, G.: Implementing Statistical Disclosure Control for Aggregated Data Released Via Remote Access, Working Paper No. 30, United Nations Statistical Commission and European Commission, joint ECE/EUROSTAT work session on statistical data confidentiality (April 2003), available at http://www.unece.org/stats/documents/2003/04/confidentiality/wp.30.e.pdf
Goldwasser, S., Micali, S.: Probabilistic Encryption and How to Play Mental Poker Keeping Secret All Partial Information. In: STOC 1982, pp. 365–377 (1982)
Raghunathan, T.E., Reiter, J.P., Rubin, D.B.: Multiple Imputation for Statistical Disclosure Limitation. Journal of Official Statistics 19(1), 1–16 (2003)
Rubin, D.B.: Discussion: Statistical Disclosure Limitation. Journal of Official Statistics 9(2), 461–469 (1993)
Shoshani, A.: Statistical databases: Characteristics, problems and some solutions. In: Proceedings of the 8th International Conference on Very Large Data Bases (VLDB 1982), pp. 208–222 (1982)
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Dwork, C., Nissim, K. (2004). Privacy-Preserving Datamining on Vertically Partitioned Databases. In: Franklin, M. (eds) Advances in Cryptology – CRYPTO 2004. CRYPTO 2004. Lecture Notes in Computer Science, vol 3152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28628-8_32
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DOI: https://doi.org/10.1007/978-3-540-28628-8_32
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