Abstract
Database privacy is an ambiguous concept, whose meaning is usually context-dependent. We give a conceptual framework for technologies in that field in terms of three dimensions, depending on whose privacy is considered: i) respondent privacy (to avoid re-identification of patients or other individuals to whom the database records refer); ii) owner privacy (to ensure that the owner must not give away his dataset); and iii) user privacy (to preserve the privacy of queries submitted by a data user). Examples are given to clarify why these are three independent dimensions. Some of the pitfalls related to combining the privacy interests of respondents, owners and users are discussed. An assessment of database privacy technologies against the three dimensions is also included.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aggarwal, C.C., Yu, P.S.: A condensation approach to privacy preserving data mining. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 183–199. Springer, Heidelberg (2004)
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: k-Anonymity: Algorithms and hardness. Technical report, Stanford University (2004)
Agrawal, R., Grandison, T., Johnson, C., Kiernan, J.: Enabling the 21st century health care information technology revolution. Communications of the ACM 50(2), 35–42 (2007)
Agrawal, R., Kiernan, J., Srikant, R., Xu, Y.: Hippocratic databases. In: Proceedings of the 28th International Conference on Very Large Databases, Hong Kong (2002)
Agrawal, R., Srikant, R.: Privacy preserving data mining. In: Proceedings of the ACM SIGMOD, pp. 439–450. ACM Press, New York (2000)
Aguilar, C., Deswarte, Y.: Single database private information retrieval schemes. In: Domingo-Ferrer, J., Franconi, L. (eds.) PSD 2006. LNCS, vol. 4302, pp. 257–265. Springer, Heidelberg (2006)
Chin, F.Y., Ozsoyoglu, G.: Auditing and inference control in statistical databases. IEEE Transactions on Software Engineering E-8, 574–582 (1982)
Chor, B., Goldreich, O., Kushilevitz, E., Sudan, M.: Private information retrieval. In: IEEE Symposium on Foundations of Computer Science (FOCS), pp. 41–50. IEEE Computer Society Press, Los Alamitos (1995)
Dalenius, T.: Finding a needle in a haystack - or identifying anonymous census records. Journal of Official Statistics 2(3), 329–336 (1986)
Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical data-oriented microaggregation for statistical disclosure control. IEEE Transactions on Knowledge and Data Engineering 14(1), 189–201 (2002)
Domingo-Ferrer, J., Sebé, F., Castellà, J.: On the security of noise addition for privacy in statistical databases. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 149–161. Springer, Heidelberg (2004)
Domingo-Ferrer, J., Torra, V.: Ordinal, continuous and heterogenerous k-anonymity through microaggregation. Data Mining and Knowledge Discovery 11(2), 195–212 (2005)
Du, W., Zhan, Z.: Using randomized response techniques for privacy-preserving data mining. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D.C, pp. 505–510 (2003)
Duncan, G.T., Mukherjee, S.: Optimal disclosure limitation strategy in statistical databases: deterring tracker attacks through additive noise. Journal of the American Statistical Association 95, 720–729 (2000)
Evfimievski, A.: Randomization in privacy-preserving data mining. SIGKDD Explorations: Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining 4(2), 43–48 (2002)
Gopal, R., Garfinkel, R., Goes, P.: Confidentiality via camouflage: the cvc approach to disclosure limitation when answering queries to databases. Operations Research 50, 501–516 (2002)
Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Lenz, R., Longhurst, J., Schulte-Nordholt, E., Seri, G., DeWolf, P.-P.: Handbook on Statistical Disclosure Control (version 1.0). In: Eurostat (CENEX SDC Project Deliverable) (2006)
Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–53. Springer, Heidelberg (2000)
Lindell, Y., Pinkas, B.: Privacy preserving data mining. Journal of Cryptology 15(3), 177–206 (2002)
Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13(6), 1010–1027 (2001)
Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technical report, SRI International (1998)
Schlörer, J.: Disclosure from statistical databases: quantitative aspects of trackers. ACM Transactions on Database Systems 5, 467–492 (1980)
Sweeney, L.: k-Anonimity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 10(5), 557–570 (2002)
Truta, T.M., Vinay, B.: Privacy protection: p-sensitive k-anonymity property. In: 2nd International Workshop on Privacy Data Management PDM 2006, p. 94. IEEE Computer Society Press, Los Alamitos (2006)
Verykios, V.S., Elmagarmid, A.K., Bertino, E., Saygin, Y., Dasseni, E.: Association rule hiding. IEEE Transactions on Knowledge and Data Engineering 16(4), 434–447 (2004)
Willenborg, L., DeWaal, T.: Elements of Statistical Disclosure Control. Springer, New York (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Domingo-Ferrer, J. (2007). A Three-Dimensional Conceptual Framework for Database Privacy. In: Jonker, W., Petković, M. (eds) Secure Data Management. SDM 2007. Lecture Notes in Computer Science, vol 4721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75248-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-75248-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75247-9
Online ISBN: 978-3-540-75248-6
eBook Packages: Computer ScienceComputer Science (R0)