References

References to Privacy-Preserving Data Management Literature

If you find any mistake or any additional citation you consider essential to the collection, please let me know by sending an email to nzhang at cs.tamu.edu.

[1] D. Agrawal and C. C. Aggarwal, On the design and quantification of privacy preserving data mining algorithms, Proceedings of the 20th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2001, pp. 247-255.

[2] R. Agrawal, D. Asonov and R. Srikant, Enabling sovereign information sharing using web services, Proceedings of the 23rd ACM SIGMOD international conference on Management of data, 2004, pp. 873-877.

[3] R. Agrawal, A. Evfimievski and R. Srikant, Information sharing across private databases, Proceedings of the 22nd ACM SIGMOD international conference on Management of data, 2003, pp. 86-97.

[4] R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules, Proceedings of the 20th international conference on Very Large Data Bases, 1994, pp. 487-499.

[5] R. Agrawal and R. Srikant, Privacy-Preserving Data Mining, Proceedings of the 19th ACM SIGMOD international conference on Management of data, 2000, pp. 439-450.

[6] R. Agrawal, R. Srikant and D. Thomas, Privacy Preserving OLAP, Proceedings of the 25th ACM SIGMOD international conference on Management of data, 2005, pp. 251 - 262.

[7] S. Agrawal and J. Haritsa, A Framework for High-Accuracy Privacy-Preserving Mining, Proceedings of the 21st international conference on Data engineering, 2005, pp. 193-204.

[8] S. Agrawal, V. Krishnan and J. Haritsa, On Addressing Efficiency Concerns in Privacy-Preserving Mining, Proceedings of the nineth international conference on Database systems for advanced applications, 2004, pp. 113-124

[9] N. Alon, M. Krivelevich and V. H. Vu, On the Concentration of Eigenvalues of Random Symmetric Matrices, Israel Journal of Mathematics, 131 (2002), pp. 259-267.

[10] C. Blake and C. Merz, UCI repository of machine learning databases, 1998.

[11] M. Blum, How to Exchange (secret) Keys, ACM Transactions on Computer Systems, 1 (1983), pp. 175-193.

[12] M. Blum and S. Micali, How to generate cryptographically strong sequences of pseudo-random bits, SIAM Journal on Computing, 13 (1984), pp. 850--863.

[13] C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin and M. Zhu, Tools for Privacy Preserving Distributed Data Mining, ACM SIGKDD Explorations, 2003.

[14] R. Conway and D. Strip, Selective partial access to a database, Proceedings of ACM/CSC-ER annual conference, 1976, pp. 85-89.

[15] T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley-Interscience, 1991.

[16] L. F. Cranor, J. Reagle and M. S. Ackerman, Beyond concern: Understanding net users' attitudes about online privacy, AT&T Labs-Research, 1999.

[17] C. Davis and W. M. Kahan, The Rotation of Eigenvectors by a Perturbatino. III, SIAM Journal on Numerical Analysis, 7 (1970), pp. 1-46.

[18] W. Du, Y. S. Han and S. Chen, Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification, Proceedings of the fourth SIAM International Conference on Data Mining, 2004, pp. 222-233.

[19] W. Du and H. Polat, Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques, Proceedings of the third IEEE international conference on Data mining, 2003, pp. 625-628.

[20] W. Du and Z. Zhan, Building decision tree classifier on private data, Proceedings of the IEEE International Conference on Privacy, Security and Data Mining, 2002, pp. 1-8.

[21] W. Du and Z. Zhan, Using randomized response techniques for privacy-preserving data mining, Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining, 2003, pp. 505-510.

[22] T. ElGamal, A public key cryptosystem and a signature scheme based on discrete logarithms, IEEE Transactions on Information Theory, 31 (1985), pp. 469-472.

[23] S. Even, O. Goldreich and A. Lempel, A Randomizing Protocol for Signing Contracts, Communications of the ACM, 28 (1985), pp. 637-647.

[24] A. Evfimievski, J. Gehrke and R. Srikant, Limiting privacy breaches in privacy preserving data mining, Proceedings of the 22nd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of database systems, 2003, pp. 211-222.

[25] A. Evfimievski, R. Srikant, R. Agrawal and J. Gehrke, Privacy Preserving Mining of Association Rules, Proceedings of the 8th ACM SIGKDD international conference on Knowledge discovery in databases and data mining, 2002, pp. 217-228.

[26] M. J. Freedman, K. Nissim and B. Pinkas, Efficient private matching and set intersection, Advances in Cryptography: Proceedings of Eurocrypt 2004, 2004.

[27] D. Fudenberg and J. Tirole, Game theory, MIT Press, 1991.

[28] O. Goldreich, The foundations of cryptography, Cambridge University Press, 2004.

[29] O. Goldreich, Foundations of Cryptography: Basic Tools, Cambridge University Press, 2001.

[30] O. Goldreich, S. Micali and A. Wigderson, How to play ANY mental game, Proceedings of the 19th annual ACM conference on Theory of computing, ACM Press, 1987, pp. 218-229.

[31] G. H. Golub and C. F. V. Loan, Matrix computation, John Hopkins University Press, 1996.

[32] J. Han and M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, 2001.

[33] S. Hettich and S. D. Bay, The UCI KDD Archive [http://kdd.ics.uci.edu], Irvine, CA. University of California, Department of Information and Computer Science, 1999.

[34] HIPAA, Health Insurance Portability and Accountability Act, in D. o. H. a. H. Services, ed., 2002.

[35] Z. Huang, W. Du and B. Chen, Deriving Private Information from Randomized Data, Proceedings of the 24th ACM SIGMOD international conference on Management of data, 2005, pp. 37 - 48.

[36] B. A. Huberman, M. Franklin and T. Hogg, Enhancing privacy and trust in electronic communities, Proceedings of the 1st ACM conference on Electronic commerce, 1999, pp. 78-86.

[37] IBM, IBM-Harris multinational customer privacy survey, 1999.

[38] N. Jefferies, C. Mitchell and M. Walker, A proposed architecture for trusted third party services, Cryptography Policy and Algorithms Conference, Springer Verlag, 1995, pp. 98-104.

[39] M. Kantarcioglu and C. Clifton, Privacy-preserving distributed mining of association rules on horizontally partitioned data, IEEE Transactions on Knowledge and Data Engineering, 16 (2004), pp. 1026-1037.

[40] M. Kantarcioglu and J. Vaidya, Privacy preserving naive bayes classifier for horizontally partitioned data, Workshop on Privacy Preserving Data Mining held in association with The third IEEE International Conference on Data Mining, 2003.

[41] H. Kargupta, S. Datta, Q. Wang and K. Sivakumar, On the privacy preserving properties of random data perturbation techniques, Proceedings of the third IEEE international conference on Data mining, 2003, pp. 99-106.

[42] L. Kaufman and P. J. Rousseeuw, Finding groups in data: an introduction to cluster analysis, John Wiley & Sons, New York, 1990.

[43] L. Kissner and D. Song, Privacy-Preserving Set Operations, Proceedings of the 25th Annual international cryptology conference, 2005.

[44] Y. Lindell and B. Pinkas, Privacy preserving data mining, Proceedings of the 12th Annual International Cryptology Conference on Advances in cryptology, 2000, pp. 36-54.

[45] M. Luby, S. Micali and C. Rackoff, How to Simultaneously Exchange a Secret Bit by Flipping a Symmertically-Biased Coin, Proceedings of the 24th Annual Symposium on the Foundations of Computer Science, 1983, pp. 11-12.

[46] D. McCullagh, Database Nation: The Upside of Zero Privacy, Reason, 6 (2004), pp. 26-35.

[47] H. Miranda, Diagonals And Eigenvalues of Sums of Hermitian Matrices. Extreme Cases, Proyecciones, 22 (2003), pp. 127-134.

[48] MPT, Ministry of Posts and Telecommunications Survey, in J. M. o. P. a. Telecommunications, ed., 1999.

[49] M. Naor and B. Pinkas, Oblivious transfer and polynomial evaluation, Proceedings of the 31st annual ACM symposium on Theory of computing, 1999, pp. 245-254.

[50] S. C. Pohlig and M. E. Hellman, An Improved Algorithm for Computing Logarithms in GF(p) and Its Cryptographic Significance, IEEE Transactions on Information Theory, 24 (1978), pp. 106-111.

[51] J. R. Quinlan, Introduction of decision trees, Machine Learning, 1 (1986), pp. 81-106.

[52] M. O. Rabin, How to Exchange Secrets by Oblivious Transfer, Aiken Computer Laboratory, Harvard University, 1981.

[53] S. Rizvi and J. Haritsa, Maintaining Data Privacy in Association Rule Mining, Proceedings of the 28th international conference on Very Large Data Bases, 2002, pp. 682-693.

[54] A. Shamir, R. L. Rivest and L. M. Adleman, Mental poker, Mathematical Gardner, Wadsworth, Belmont, California, 1981, pp. 37-43.

[55] G. W. Stewart and J. Sun, Matrix perturbation theory, Academic Press, 1990.

[56] J. F. Traub, Y. Yemini and H. Wo\acute{z}niakowski, The Statistical Security of a Statistical Database, ACM Transactions on Database Systems, 9 (1984), pp. 672-679.

[57] J. Vaidya and C. Clifton, Privacy preserving association rule mining in vertically partitioned data, Proceedings of the 8th ACM SIGKDD international conference on Knowledge discovery and data mining, 2002, pp. 639-644.

[58] J. Vaidya and C. Clifton, Privacy preserving naive Bayes classifier for vertically partitioned data, Proceedings of the 4th SIAM Conference on Data mining, 2004, pp. 330-334.

[59] J. Vaidya and C. Clifton, Privacy-Preserving Top-K Queries, Proceedings of the 21st International Conference on Data Engineering, 2005.

[60] L. Wang, S. Jajodia and D. Wijesekera, Securing OLAP data cubes against privacy breaches, Proceedings of IEEE Symposium On Security and Privacy, 2004.

[61] L. Wang, D. Wijesekera and S. Jajodia, Cardinality-based inference control in data cubes, Journal of Computer Security, 12 (2004), pp. 655 - 692.

[62] S. L. Warner, Randomized response: a survey technique for eliminating evasive answer bias, Journal of the American Statistical Association, 60 (1965), pp. 63-69.

[63] A. F. Westin, Consumers, Privacy, and Survey Research, presented at Council of American Survey Research Organizations Annual Conference, 2003.

[64] A. F. Westin, Privacy and Freedom, Atheneum, New York, 1967.

[65] E. Wigner, Characteristic Vectors of Bordered Matrices with Infinite Dimensions, Annals of Mathematics, 62 (1955), pp. 548-564.

[66] R. N. Wright and Z. Yang, Privacy-preserving Bayesian network structure computation on distributed heterogeneous data, Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004, pp. 713-718.

[67] W. Xu, R. Sekar, I. V. Ramakrishnan and V. N. Venkatakrishnan, An Approach for Realizing Privacy-Preserving Web-Based Services, Special interest tracks and posters of the 14th international conference on World Wide Web, 2005.

[68] Z. Yang, S. Zhong and R. N. Wright, Privacy-Preserving Classification of Customer Data without Loss of Accuracy, Proceedings of the fifth SIAM international conference on Data mining, 2005, pp. 92-102.

[69] A. C. Yao, How to generate and exchange secrets, Proceedings of the 27th Annual Symposium on Foundations of computer science, IEEE Press, 1986, pp. 162-167.

[70] A. C. Yao, Protocols for secure computations (extended abstract), 23rd Annual Symposium on Foundations of Computer Science, IEEE, 1982, pp. 160--164.

[71] A. C. Yao, Theory and application of trapdoor functions, 23rd Annual Symposium on Foundations of Computer Science, 1982, pp. 80--91.

[72] N. Zhang, Privacy-Preserving Data Mining, Ph.D. Dissertation, Department of Computer Science, Texas A&M University, College Station, in preparation, 2006.

[73] N. Zhang, S. Wang and W. Zhao, A New Scheme on Privacy Preserving Association Rule Mining, Proceedings of the 8th European conference on Principles and practice of knowledge discovery in databases, 2004, pp. 484-495.

[74] N. Zhang, S. Wang and W. Zhao, A New Scheme on Privacy Preserving Data Classification, Proceedings of the 11th ACM SIGKDD international conference on Knowledge discovery and data mining, 2005.

[75] N. Zhang and W. Zhao, Distributed privacy preserving information sharing, Proceedings of the 31st international conference on Very large data bases, 2005, pp. 889 - 900.

[76] N. Zhang, W. Zhao and J. Chen, Cardinality-based inference control in OLAP systems: an information theoretic approach, Proceedings of the 7th ACM international workshop on Data warehousing and OLAP, 2004.

[77] N. Zhang, W. Zhao and J. Chen, Performance measurements for privacy preserving data mining, Proceedings of the 9th Pacific-Asia conference on Knowledge discovery and data mining, 2005, pp. 43-49.

[78] Z. Zheng, R. Kohavi and L. Mason, Real World Performance of Association Rule Algorithms, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 401-406.

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