Subscribe

Issue No.07 - July (2012 vol.24)

pp: 1170-1185

Mehmet Ercan Nergiz , Zirve University, Gaziantep

Abdullah Ercüment Çiçek , Case Western Reserve University, Cleveland

Thomas B. Pedersen , The Scientific and Technological Research Council of Turkey (TÜBİTAK), Izmit

Yücel Saygın , Sabanci University, Orhanli, Tuzla, Istanbul

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.44

ABSTRACT

Secure multiparty protocols have been proposed to enable noncolluding parties to cooperate without a trusted server. Even though such protocols prevent information disclosure other than the objective function, they are quite costly in computation and communication. The high overhead motivates parties to estimate the utility that can be achieved as a result of the protocol beforehand. In this paper, we propose a look-ahead approach, specifically for secure multiparty protocols to achieve distributed k-anonymity, which helps parties to decide if the utility benefit from the protocol is within an acceptable range before initiating the protocol. The look-ahead operation is highly localized and its accuracy depends on the amount of information the parties are willing to share. Experimental results show the effectiveness of the proposed methods.

INDEX TERMS

Secure multiparty computation, distributed k-anonymity, privacy, security.

CITATION

Mehmet Ercan Nergiz, Abdullah Ercüment Çiçek, Thomas B. Pedersen, Yücel Saygın, "A Look-Ahead Approach to Secure Multiparty Protocols",

*IEEE Transactions on Knowledge & Data Engineering*, vol.24, no. 7, pp. 1170-1185, July 2012, doi:10.1109/TKDE.2011.44REFERENCES

- [1] R.J. Bayardo and R. Agrawal, "Data Privacy through Optimal K-Anonymization,"
Proc. 21st Int'l Conf. Data Eng. (ICDE '05), pp. 217-228, 2005.- [2] C. Blake and C.J. Merz, "UCI Repository of Machine Learning Databases," http://www.ics.uci.edu/mlearnMLRepository. html , Univ. of California, Irvine, Dept. of Information and Computer Sciences, 2012.
- [3] B.-C. Chen, K. LeFevre, and R. Ramakrishnan, "Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge,"
Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB '07), pp. 770-781, 2007.- [4] J. Domingo-Ferrer and V. Torra, "Ordinal, Continuous and Heterogeneous K-Anonymity through Microaggregation,"
Data Mining and Knowledge Discovery, vol. 11, no. 2, pp. 195-212, 2005.- [5] W. Feller,
An Introduction to Probability Theory and Its Applications, vol. 1, Wiley, 1968.- [6] S.R. Ganta, S.P. Kasiviswanathan, and A. Smith, "Composition Attacks and Auxiliary Information in Data Privacy,"
Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '08), pp. 265-273, http://doi.acm.org/10.11451401890. 1401926 , 2008.- [7] G. Ghinita, P. Karras, P. Kalnis, and N. Mamoulis, "Fast Data Anonymization with Low Information Loss,"
Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB '07), pp. 758-769, 2007.- [8] O. Goldreich,
The Foundations of Cryptography, vol. 2, Cambridge Univ. Press, http://www.wisdom.weizmann.ac.il/~oded/PSBookFrag enc.ps, 2004.- [9] V.S. Iyengar, "Transforming Data to Satisfy Privacy Constraints,"
Proc. Eighth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '02), pp. 279-288, 2002.- [10] W. Jiang and C. Clifton, "Privacy-Preserving Distributed $k$ -Anonymity,"
Proc. 19th Ann. IFIP WG 11.3 Working Conf. Database and Applications Security, Aug. 2005.- [11] W. Jiang and C. Clifton, "A Secure Distributed Framework for Achieving $k$ -Anonymity,"
VLDB J., special issue on privacy-preserving data management, vol. 15, pp. 316-333, Sept. 2006.- [12] M. Kantarclu and C. Clifton, "Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data,"
IEEE Trans. Knowledge and Data Eng., vol. 16, no. 9, pp. 1026-1037, Sept. 2004.- [13] D. Kifer and J. Gehrke, "Injecting Utility into Anonymized Datasets,"
Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '06), pp. 217-228, 2006.- [14] S.N. Lahiri, A. Chatterjeea, and T. Maiti, "Normal Approximation to the Hypergeometric Distribution in Nonstandard Cases and a Sub-Gaussian Berryesseen Theorem,"
J. Statistical Planning and Inference, vol. 137, no. 11, pp. 3570-3590, http://dx.doi.org/10. 1016j.jspi.2007.03.033 , Nov. 2007.- [15] B. Levin, "A Representation for Multinomial Cumulative Distribution Functions,"
The Annals of Statistics, vol. 9, no. 5, pp. 1123-1126, http://www.jstor.org/stable2240628, 1981.- [16] N. Li and T. Li, "T-Closeness: Privacy Beyond K-Anonymity and L-Diversity,"
Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE '07), Apr. 2007.- [17] Y. Lindell and B. Pinkas, "Privacy Preserving Data Mining,"
J. Cryptology, vol. 15, pp. 36-54, 2000.- [18] A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam, "$\ell$ -Diversity: Privacy beyond $k$ -Anonymity,"
Proc. IEEE 22nd Int'l Conf. Data Eng. (ICDE '06), Apr. 2006.- [19] D.J. Martin, D. Kifer, A. Machanavajjhala, J. Gehrke, and J.Y. Halpern, "Worst-Case Background Knowledge for Privacy-Preserving Data Publishing,"
Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE '07), Apr. 2007.- [20] M.E. Nergiz, M. Atzori, and C. Clifton, "Hiding the Presence of Individuals in Shared Databases,"
Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '07), June 2007.- [21] M.E. Nergiz and C. Clifton, "Thoughts on K-Anonymization,"
Data and Knowledge Eng., vol. 63, no. 3, pp. 622-645, http://dx.doi.org/10.1016j.datak.2007.03.009 , Dec. 2007.- [22] A. Øhrn and L. Ohno-Machado, "Using Boolean Reasoning to Anonymize Databases,"
Artificial Intelligence in Medicine, vol. 15, no. 3, pp. 235-254, http://dx.doi.org/10.1016S0933-3657(98)00056-6 , Mar. 1999.- [23] V. Poosala and Y.E. Ioannidis, "Selectivity Estimation without the Attribute Value Independence Assumption,"
Proc. 23rd Int'l Conf. Very Large Data Bases (VLDB '97), pp. 486-495, 1997.- [24] P. Samarati, "Protecting Respondent's Identities in Microdata Release,"
IEEE Trans. Knowledge and Data Eng., vol. 13, no. 6, pp. 1010-1027, Nov./Dec. 2001.- [25] S.J. Schwager, "Bonferroni Sometimes Loses,"
The Am. Statistician, vol. 38, no. 3, pp. 192-197, http://www.jstor.org/stable2683651, 1984.- [26] L. Sweeney, "Achieving $k$ -Anonymity Privacy Protection Using Generalization and Suppression,"
Int'l J. Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 571-588, 2002.- [27] L. Sweeney, "k-Anonymity: A Model for Protecting Privacy,"
Int'l J. Uncertainty, Fuzziness Knowledge-Based Systems, vol. 10, no. 5, pp. 557-570, 2002.- [28] J. Vaidya, "Privacy Preserving Data Mining Over Vertically Partitioned Data," PhD dissertation, Dept. of Computer Sciences, Purdue Univ., West Lafayette, Indiana, http://www.cs.purdue. edu/homes/jsvaidya thesis.pdf, 2004.
- [29] R.C.-W. Wong, J. Li, A.W.-C. Fu, and K. Wang, "($\alpha$ , k)-Anonymity: An Enhanced K-Anonymity Model for Privacy Preserving Data Publishing,"
Proc. 12th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '06), pp. 754-759, 2006.- [30] X. Xiao and Y. Tao, "M-Invariance: Towards Privacy Preserving Re-Publication of Dynamic Datasets,"
Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '07), pp. 689-700, 2007.- [31] S. Zhong, Z. Yang, and R.N. Wright, "Privacy-Enhancing K-Anonymization of Customer Data,"
Proc. 24th ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems (PODS '05), pp. 139-147, 2005. |