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Issue No.02 - February (2009 vol.21)
pp: 253-258
Jaideep Vaidya , Rutgers University, Newark
Christopher W. Clifton , Purdue University, West Lafayette
Given a large integer data set shared vertically by two parties, we consider the problem of securely computing a score separating the k{\rm th} and the (k + 1){\rm th} element. An efficient secure protocol is developed to compute such a score while revealing little additional information. The proposed protocol is implemented using the Fairplay system and experimental results are reported. We show a real application of this protocol as a component used in the secure processing of top-k queries over vertically partitioned data.
Privacy, security, k{\rm th} element score, top-k queries.
Jaideep Vaidya, Christopher W. Clifton, "Privacy-Preserving Kth Element Score over Vertically Partitioned Data", IEEE Transactions on Knowledge & Data Engineering, vol.21, no. 2, pp. 253-258, February 2009, doi:10.1109/TKDE.2008.167
[1] R. Fagin, “Combining Fuzzy Information from Multiple Systems,” Proc. 15th ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems (PODS '96), pp. 216-226, Fagin96.html, June 1996.
[2] R. Fagin, “Combining Fuzzy Information from Multiple Systems,” J. Computer and System Sciences, (Special issue for selected papers from the 1996 ACM Symp. Principles of Database Systems), pp.83-99, 1999.
[3] N. Zhang and W. Zhao, “Distributed Privacy Preserving Information Sharing,” Proc. 31st Int'l Conf. Very Large Data Bases (VLDB '05), pp. 889-900, 2005.
[4] W. Jiang and C. Clifton, “Ac-Framework for Privacy-Preserving Collaboration,” Proc. Seventh SIAM Int'l Conf. Data Mining (SDM), 2007.
[5] M. Shaneck, Y. Kim, and V. Kumar, “Privacy Preserving Nearest Neighbor Search,” Proc. ICDM Workshops, pp. 541-545, 2006.
[6] T.M. Cover and P. Hart, “Nearest Neighbor Pattern Classification,” IEEE Trans. Information Theory, vol. 13, pp. 21-27, Jan. 1967.
[7] L. Ertoz, M. Steinbach, and V. Kumar, “A New Shared Nearest Neighbor Clustering Algorithm and Its Applications,” Proc. Text Mine, First SIAM Int'l Conf. Data Mining (SDM '01), Workshop Clustering High Dimensional Data and Its Applications, 2001.
[8] R.A. Jarvis and E.A. Patrick, “Clustering Using a Similarity Measure Based on Shared Nearest Neighbors,” IEEE Trans. Computers, vol. 22, no. 11, pp. 1025-1034, Nov. 1973.
[9] M.M. Breunig, H.-P. Kriegel, R.T. Ng, and J. Sander, “LOF:Identifying Density-Based Local Outliers,” Proc. ACM SIGMOD'00, W. Chen, J.F. Naughton, and P.A. Bernstein, eds., pp. 93-104, 2000.
[10] R. Fagin, “Fuzzy Queries in Multimedia Database Systems,” Proc. 17th ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems (PODS '98), pp. 1-10, Fagin98.html, June 1998.
[11] S. Chaudhuri and L. Gravano, “Evaluating Top-$k$ Selection Queries,” Proc. 25th Int'l Conf. Very Large Data Bases (VLDB'99), pp.397-410, , Sept. 1999.
[12] N. Bruno, L. Gravano, and A. Marian, “Evaluating Top-$k$ Queries over Web-Accessible Databases,” Proc. 18th Int'l Conf. Data Eng. (ICDE '02), pp. 369-380, 00994751.pdf, Feb./Mar. 2002.
[13] A.C. Yao, “How to Generate and Exchange Secrets,” Proc. 27th IEEE Symp. Foundations of Computer Science (FOCS '86), pp.162-167, 1986.
[14] O. Goldreich, The Foundations of Cryptography, vol. 2, ch. General Cryptographic Protocols, Cambridge Univ. Press,, 2004.
[15] R. Fagin, A. Lotem, and M. Naor, “Optimal Aggregation Algorithms for Middleware,” Proc. 20th ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems (PODS '01), pp. 102-113,, May 2001.
[16] D. Donjerkovic and R. Ramakrishnan, “Probabilistic Optimization of Top $n$ Queries,” Proc. 25th Int'l Conf. Very Large DataBases (VLDB '99), pp. 411-422, http://portal.acm.orgcitation.cfm?id=671510& dl=ACM&coll=GUIDE& CFID=11111111&CFTOKEN=2222222 , 1999.
[17] I.F. Ilyas, W.G. Aref, and A.K. Elmagarmid, “Supporting Top-$k$ Join Queries in Relational Databases,” Proc. 29th Int'l Conf. Very Large Data Bases (VLDB '03), pp. 754-765, , Sept. 2003.
[18] “Special Section on Privacy and Security” SIGKDD Explorations, vol. 4, no. 2, pp. i-48, issue4-2contents.htm, Jan. 2003.
[19] J. Vaidya, C. Clifton, and M. Zhu, Privacy-Preserving Data Mining, first ed., ser. Advances in Information Security, vol. 19, Springer-Verlag, 0,11855,4-40356-72-52496494-0,00.html , 2005.
[20] G. Aggarwal, N. Mishra, and B. Pinkas, “Secure Computation ofthe $k{\rm th}$ -Ranked Element,” Proc. IACR Int'l Conf. Theory and Applications of Cryptographic Techniques (EUROCRYPT '04), May 2004.
[21] D. Malkhi, N. Nisan, B. Pinkas, and Y. Sella, “Fairplay—A Secure Two-Party Computation System,” Proc. 13th USENIX Security Symp., pp. 287-302, 2004.
[22] J. Vaidya and C. Clifton, “Secure Set Intersection Cardinality withApplication to Association Rule Mining,” J. Computer Security, vol. 13, no. 4, pp. 593-622, Nov. 2005.
[23] R. Agrawal, A. Evfimievski, and R. Srikant, “Information Sharing across Private Databases,” Proc. ACM SIGMOD '03,, June 2003.
[24] M.J. Freedman, K. Nissim, and B. Pinkas, “Efficient Private Matching and Set Intersection,” Proc. Int'l Conf. Theory and Applications of Cryptographic Techniques (EUROCRYPT '04), May 2004.
[25] M. Kantarciogˇlu 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, 09k1026abs.htm, Sept. 2004.
[26] M. Bawa, R.J. Bayardo Jr, and R. Agrawal, “Privacy-Preserving Indexing of Documents on the Network,” Proc. 29th Int'l Conf. Very Large Data Bases (VLDB '03), pp. 922-933, , Sept. 2003.
[27] S.C. Pohlig and M.E. Hellman, “An Improved Algorithm for Computing Logarithms over GF(p) and Its Cryptographic Significance,” IEEE Trans. Information Theory, vol. IT-24, pp.106-110, 1978.
[28] B. Goethals, S. Laur, H. Lipmaa, and T. Mielikäinen, “On Secure Scalar Product Computation for Privacy-Preserving Data Mining,” Proc. Seventh Ann. Int'l Conf. Information Security and Cryptology (ICISC '04), C. Park and S. Chee, eds., vol. 3506, pp.104-120, Dec. 2004.
[29] M. Fischlin, “A Cost-Effective Pay-Per-Multiplication Comparison Method for Millionaires,” RSA Security 2001 Cryptographer's Track LNCS, vol. 2020, Springer-Verlag, pp. 457-471, 2001.
[30] I. Ioannidis and A. Grama, “An Efficient Protocol for Yao's Millionaires' Problem,” Proc. Hawaii Int'l Conf. System Sciences (HICSS-36), pp. 205-210, Jan. 2003.
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