| [1] Liu J, Pan Y, Wang K, Han J. Mining frequent item sets byopportunistic projection. In Proc. the 8th ACM SIGKDDConf. Knowledge Discovery and Data Mining, Edmonton,Canada, July 2002, pp.229-238.[2] Narayanan A, Shmatikov V. How to break anonymity of theNetflix prize dataset. ArXiv Computer Science e-prints, Vol-ume: abs/cs/061, December 2005, pp.1-10.[3] Adar E. User 4XXXXX9: Anonymizing query logs. In QueryLog Analysis Workshop at the 16th Int. World Wide WebConf., Banff, Canada, May 2007.[4] Xiong L, Agichtein E. Towards privacy-preserving query logpublishing. In Query Log Analysis Workshop at Int. WorldWide Web Conf., Banff, Canada, May 2007.[5] He Y, Naughton J. Anonymization of set-valued data via top-down, local generalization. In Proc. Very Large Data BasesConf., Lyon, France, August 2009, pp.934-945.[6] Ghinita G, Tao Y, Kalnis P. On the anonymization of sparsehigh-dimensional data. In Proc. Int. Conf. Data Engineer-ing, Cancun, Mexico, April 2008, pp.715-724.[7] Terrovitis M, Mamoulis N, Kalnis P. Privacy preservinganonymization of set-valued data. In Proc. the 34th VeryLarge Data Bases Conf., Auckland, New Zealand, Aug. 2008,pp.115-125.[8] Xu Y, Wang K, Fu A, Yu P S. Anonymizing transactiondatabases for publication. In Proc. the 14th ACM SIGKDDConf. Knowledge Discovery and Data Mining, Las Vegas,USA, August 2008, pp.767-775.[9] Samarati P, Sweeney L. Generalizing data to provideanonymity when disclosing information. In Proc. ACMSIGMOD-SIGACT-SIGART Symposium on Principles ofDatabase Systems, Seattle, USA, June 1998, p.188.[10] Aggarwal C C. On k-anonymity and the curse of dimensiona-lity. In Proc. the 31st Very Large Data Bases Conf., Trond-heim, Norway, August 2005, pp.901-909.[11] LeFevre K, DeWitt D, Ramakrishnan R. Mondrian multidi-mensional k-anonymity. In Proc. Int. Conf. Data Engineer-ing, Atlanta, USA, April 2006, pp.25.[12] Liu J, Wang K. On optimal anonymization for `+-diversity.In Proc. Int. Conf. Data Engineering, Long Beach, USA,March 2010, pp.213-224.[13] Machanavajjhala A, Gehrke J, Kifer D, VenkitasubramaniamM. `-Diversity: Privacy beyond k-anonymity. In Proc. Int.Conf. Data Engineering, Atlanta, USA, April 2006, p.24.[14] Machanavajjhala A, Gehrke J, G?otz M. Data publishingagainst realistic adversaries. In Proc. Very Large Data BasesConf., Lyon, France, August 2009, pp.790-801.[15] Martin D J, Kifer D, Machanavajjhala A, Gehrke J. Worst-case background knowledge for privacy preserving data pub-lishing. In Proc. Int. Conf. Data Engineering, Istanbul,Turkey, April 2007, pp.126-135.[16] Cormode G, Srivastava D, Yu T, Zhang Q. Anonymizing bi-partite graph data using safe groupings. In Proc. the 34thVery Large Data Bases Conf., Auckland, New Zealand, Au-gust 2008, pp.833-844.[17] Evfimievski A, Srikant R, Agrawal R, Gehrke J. Privacy pre-serving mining of association rules. In Proc. the 8th ACMSIGKDD Conf. Knowledge Discovery and Data Mining, Ed-monton, Canada, July 2002, pp.217-228.[18] Verykios V S, Elmagarmid A K, Bertino E, Saygin Y, DasseniE. Association rule hiding. Trans. Knowledge and Data En-gineering, April 2004, 16(4): 434-447.[19] Atzori M, Bonchi F, Giannotti F, Pedreschi D. Anonymitypreserving pattern discovery. VLDB Journal, July 2008,17(4):703-727.[20] Dwork C. Differential privacy. In Proc. the 33rd Int. Col-loquium on Automata, Languages and Programming, Venice,Italy, July 2006, Part II, pp.1-12.[21] Korolova A, Kenthapadi K, Mishra N, Ntoulas A. Releasingsearch queries and clicks privately. In Proc. Int. World WideWeb Conf., Madrid, Spain, April 2009, pp.171-180.[22] Xiao Y, Xiong L, Yuan C. Differentially private data releasethrough multidimensional partitioning. In Secure Data Man-agement Workshop at Very Large Data Bases Conf., Singa-pore, September 2010, pp.150-168.[23] Chen R, Mohammed N, Fung B C M, Desai B C, Xiong L.Publishing set-valued data via differential privacy. In Proc.the 37th Very Large Data Bases Conf., Seattle, USA, August2011, pp.1087-1098.[24] Iyengar V. Transforming data to satisfy privacy constraints.In Proc. the 8th ACM SIGKDD Conf. Knowledge Discoveryand Data Mining, Edmonton, Canada, July 2002, pp.279-288.[25] Xu J, Wang W, Pei J, Wang X, Shi B, Fu A. Utility-basedanonymization using local recoding. In Proc. the 12th ACMSIGKDD Conf. Knowledge Discovery and Data Mining,Philadelphia, USA, August 2006, pp.785-790.[26] Bayardo R J, Agrawal R. Data privacy through optimal k-anonymization. In Proc. Int. Conf. Data Engineering,Tokyo, Japan, April 2005, pp.217-228.[27] Zheng Z, Kohavi R, Mason L. Real world performance of as-sociation rule algorithms. In Proc. the 7th ACM SIGKDDConf. Knowledge Discovery and Data Mining, San Francisco,USA, August 2001, pp.401-406.[28] Xiao X, Tao Y. Anatomy: Simple and effective privacy preser-vation. In Proc. the 32nd Very Large Data Bases Conf.,Seoul, Korea, Sept. 2006, pp.139-150. |