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1 References 1. U.S. Dept. of Health and Human Services: Standards for privacy of individually identifiable health information; Final Rule, Federal Register 45 CFR: Pt. 160 and National Institutes of Health: Final NIH statement on sharing research data, NOT OD O. Abul, M. Atzori, F. Bonchi, and F. Giannotti. Hiding sequences. In Proceedings of the 23rd International Conference on Data Engineering Workshops (ICDEW), pages , C. C. Aggarwal and P. S. Yu. Privacy Preserving Data Mining: Models and Algorithms. Springer Verlag, R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages , R. Agrawal and J. C. Shafer. Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering, 8(1): , R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Databases (VLDB), pages , R. Agrawal and R. Srikant. Privacy preserving data mining. SIGMOD Record, 29(2): , A. Amiri. Dare to share: Protecting sensitive knowledge with data sanitization. Decision Support Systems, 43(1): , M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim, and V. S. Verykios. Disclosure limitation of sensitive rules. In Proceedings of the 1999 IEEE Knowledge and Data Engineering Exchange Workshop (KDEX), pages 45 52, R. Bayardo. Efficiently mining long patterns from databases. Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, T. Calders. Computational complexity of itemset frequency satisfiability. In Proceedings of the 23rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS), pages , T. Calders. The complexity of satisfying constraints on databases of transactions. Acta Informatica, 44(7): , T. Calders. Itemset frequency satisfiability: Complexity and axiomatization. Theoretical Computer Science, 394(1):84 111, L. Chang and I. S. Moskowitz. Parsimonious downgrading and decision trees applied to the inference problem. In Proceedings of the 1998 Workshop on New Security Paradigms (NSPW), pages 82 89, K. Chen and L. Liu. Privacy preserving data classification with rotation perturbation. In Proceedings of the 5th IEEE International Conference on Data Mining, pages ,

2 146 References 17. X. Chen, M. Orlowska, and X. Li. A new framework of privacy preserving data sharing. In Proceedings of the 4th IEEE International Workshop on Privacy and Security Aspects of Data Mining, pages 47 56, C. W. Clifton and D. Marks. Security and privacy implications of data mining. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pages 15 19, Feb J. C. da Silva and M. Klusch. Inference on distributed data clustering. In Proceedings of the 4th International Conference on Machine Learning and Data Mining in Pattern Recognition, pages , E. Dasseni, V. S. Verykios, A. K. Elmagarmid, and E. Bertino. Hiding association rules by using confidence and support. In Proceedings of the 4th International Workshop on Information Hiding, pages , C. Farkas and S. Jajodia. The inference problem: A survey. SIGKDD Explorations, 4(2):6 11, M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP Completeness (Series of Books in the Mathematical Sciences). W. H. Freeman, January A. Gkoulalas-Divanis and V. S. Verykios. An integer programming approach for frequent itemset hiding. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM), pages , A. Gkoulalas-Divanis and V. S. Verykios. A hybrid approach to frequent itemset hiding. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pages , A. Gkoulalas-Divanis and V. S. Verykios. A parallelization framework for exact knowledge hiding in transactional databases. In Proceedings of the 23rd International Information Security Conference (SEC), pages , A. Gkoulalas-Divanis and V. S. Verykios. Exact knowledge hiding through database extension. IEEE Transactions on Knowledge and Data Engineering, 21(5): , A. Gkoulalas-Divanis and V. S. Verykios. Hiding sensitive knowledge without side effects. Knowledge and Information Systems, 20(3): , A. Gkoulalas-Divanis and V. S. Verykios. Privacy Preserving Data Mining: How far can we go?, pages Handbook of Research on Data Mining in Public and Private Sectors: Organizational and Governmental Applications. IGI Global, Accepted. 29. GLPK. GNU GLPK 4.32 User s Manual. Free Software Foundation, Inc., Boston, MA, Available at B. Goethals. The fimi repository, Available at M. Grean and M. J. Shaw. Supply-Chain Partnership between P&G and Wal-Mart, chapter 8, pages Integrated Series in Information Systems. Springer Verlag, C. Gueret, C. Prins, and M. Sevaux. Applications of Optimization with Xpress MP. Dash Optimization Ltd., Y. Guo, Y. Tong, S. Tang, and D. Yang. A fp-tree-based method for inverse frequent set mining. In Proceedings of the 23rd British National Conference on Databases (BNCOD), pages , E. H. Han, G. Karypis, and V. Kumar. Scalable parallel data mining for association rules. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages , C. T. Heun. Wal-Mart and Other Companies Reassess their Data-Sharing Strategies. Information Week, May I. ILOG. CPLEX 9.0 User s Manual. Mountain View, CA, Oct Available at G. Jagannathan, K. Pillaipakkamnatt, and R. N. Wright. A new privacy preserving distributed k clustering algorithm. In Proceedings of the 2006 SIAM International Conference on Data Mining (SDM), S. Jha, L. Kruger, and P. McDaniel. Privacy preserving clustering. In Proceedings of the 10th European Symposium on Research in Computer Security (ESORICS), pages , 2005.

3 References G. Karypis and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal of Scientific Computing, 20(1): , A. Katsarou, A. Gkoulalas-Divanis, and V. S. Verykios. Reconstruction based classification rule hiding through controlled data modification. In Proceedings of the 5th IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI), R. Kohavi, C. Brodley, B. Frasca, L. Mason, and Z. Zheng. KDD Cup 2000 organizers report: Peeling the onion. SIGKDD Explorations, 2(2):86 98, G. Lee, C. Y. Chang, and A. L. P. Chen. Hiding sensitive patterns in association rules mining. In Proceedings of the 28th International Computer Software and Applications Conference (COMPSAC), pages , Y. Lindell and B. Pinkas. Privacy preserving data mining. Journal of Cryptology, 15(3):36 54, D. Luenberger. Introduction to Linear and Non Linear Programming. Addison Wesley, M. D. Mailman, M. Feolo, and Y. J. et. al. The ncbi dbgap database of genotypes and phenotypes. Nature Genetics, 39: , H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1(3): , S. Menon, S. Sarkar, and S. Mukherjee. Maximizing accuracy of shared databases when concealing sensitive patterns. Information Systems Research, 16(3): , T. Mielikainen. On inverse frequent set mining. In Proceedings of the 2nd Workshop on Privacy Preserving Data Mining, pages 18 23, M. Morgenstern. Controlling logical inference in multilevel database systems. In Proceedings of the IEEE Symposium on Security and Privacy, pages , G. V. Moustakides and V. S. Verykios. A max min approach for hiding frequent itemsets. In Workshops Proceedings of the 6th IEEE International Conference on Data Mining (ICDM), pages , G. V. Moustakides and V. S. Verykios. A maxmin approach for hiding frequent itemsets. Data and Knowledge Engineering, 65(1):75 89, J. Natwichai, X. Li, and M. Orlowska. Hiding classification rules for data sharing with privacy preservation. In Proceedings of the 7th International Conference on Data Warehousing and Knowledge Discovery (DAWAK), pages , J. Natwichai, X. Li, and M. Orlowska. A reconstruction based algorithm for classiciation rules hiding. In Proceedings of the 17th Australasian Database Conference (ADC), pages 49 58, S. R. M. Oliveira and O. R. Zaïane. Privacy preserving frequent itemset mining. In Proceedings of the 2002 IEEE International Conference on Privacy, Security and Data Mining (CRPITS), pages 43 54, S. R. M. Oliveira and O. R. Zaïane. Protecting sensitive knowledge by data sanitization. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM), pages , S. R. M. Oliveira and O. R. Zaïane. Achieving privacy preservation when sharing data for clustering. In Proceedings of the 2004 SIAM International Conference on Data Mining (SDM), S. R. M. Oliveira and O. R. Zaïane. Privacy preserving clustering by object similarity based representation and dimensionality reduction transformation. In Proceedings of the 2nd IEEE International Conference on Data Mining (ICDM), pages 21 30, E. Pontikakis, Y. Theodoridis, A. Tsitsonis, L. Chang, and V. S. Verykios. A quantitative and qualitative analysis of blocking in association rule hiding. In Proceedings of the 2004 ACM Workshop on Privacy in the Electronic Society (WPES), pages 29 30, E. D. Pontikakis, A. A. Tsitsonis, and V. S. Verykios. An experimental study of distortion based techniques for association rule hiding. In Proceedings of the 18th Conference on Database Security (DBSEC), pages , 2004.

4 148 References 60. M. Reddy and R. Y. Wang. Estimating data accuracy in a federated database environment. In Proceedings of 6th International Conference on Information Systems and Management of Data (CISMOD), pages , S. Rizvi and J. R. Haritsa. Maintaining data privacy in association rule mining. In Proceedings of the 28th International Conference on Very Large Databases (VLDB), S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 2nd edition, Y. Saygin, V. S. Verykios, and C. W. Clifton. Using unknowns to prevent discovery of association rules. ACM SIGMOD Record, 30(4):45 54, Y. Saygin, V. S. Verykios, and A. K. Elmagarmid. Privacy preserving association rule mining. In Proceedings of the 2002 International Workshop on Research Issues in Data Engineering: Engineering E Commerce/E Business Systems (RIDE), pages , D. A. Simovici and C. Djeraba. Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics. Springer Publishing Company, Incorporated, X. Sun and P. S. Yu. A border based approach for hiding sensitive frequent itemsets. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), pages , X. Sun and P. S. Yu. Hiding sensitive frequent itemsets by a border based approach. Computing science and engineering, 1(1):74 94, P. N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison Wesley, T. G. U. Gunay. Association rule hiding over data streams. Information Technology and Control, 38: J. Vaidya, C. W. Clifton, and Y. M. Zhu. Privacy Preserving Data Mining. Springer Verlag, V. S. Verykios, E. Bertino, I. N. Fovino, L. P. Provenza, Y. Saygin, and Y. Theodoridis. State of the art in privacy preserving data mining. ACM SIGMOD Record, 33(1):50 57, V. S. Verykios, A. K. Emagarmid, E. Bertino, Y. Saygin, and E. Dasseni. Association rule hiding. IEEE Transactions on Knowledge and Data Engineering, 16(4): , V. S. Verykios and A. Gkoulalas-Divanis. A Survey of Association Rule Hiding Methods for Privacy, chapter 11, pages Privacy Preserving Data Mining: Models and Algorithms. Springer Berlin Heidelberg, K. Wang, B. C. M. Fung, and P. S. Yu. Template based privacy preservation in classification problems. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), pages , S.-L. Wang. Maintenance of sanitizing informative association rules. Expert Systems with Applications, 36(2): , S. L. Wang and A. Jafari. Using unknowns for hiding sensitive predictive association rules. In Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration (IRI), pages , S.-L. Wang, B. Parikh, and A. Jafari. Hiding informative association rule sets. Expert Systems with Applications, 33(2): , Y. Wang and X. Wu. Approximate inverse frequent itemset mining: Privacy, complexity, and approximation. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), pages , Y. H. Wu, C. M. Chiang, and A. L. P. Chen. Hiding sensitive association rules with limited side effects. IEEE Transactions on Knowledge and Data Engineering, 19(1):29 42, O. R. Zaïane, M. El-Hajj, and P. Lu. Fast parallel association rule mining without candidacy generation. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pages , 2001.

5 Index Accuracy measure, 63, 65, 72 Affected border, 51 Aggregate approach, 31 Architectural layout, 74, 83 Articulation points, 123, 124 Association rule, 6, 9 confidence, 9, 11 support, 9, 10 Binary integer programming, 71 Blanket approach, 69 Border, 12 negative border, 11 positive border, 11 Border revision, 41 Border theory, 11 Border variable, 127 Challenges, 7 Classification rule hiding, 5, 8, 21 parsimonious downgrading, 21 reconstruction approaches, 21, 22 suppression approaches, 21, 22 Constraint set, 86 Constraints degree reduction, 76, 105 Constraints graph, 124, 125 Constraints matrix, 123 Constraints satisfaction problem (def), 63 Constraints-by-transactions matrix, 66, 69 Correlation rule hiding, 144 Cover, see Generalized cover Data disclosure, 3 5, 7 Data hiding, 5 Data sanitization, vii, 5 7, Data sharing, 3, 4 Data streams, 144 Data utility, ix, 22, 31, 36, 93 Database inference control, 7 Database quality, 98 Database reconstruction, 143 De-identification, 4 Dedication, v Disaggregate approach, 31 Distance measure, 73, 98 Empty transaction, see Null transaction Estimator, 133 Feasible constraint set, 86 Fuzification, 6, 18, 35 Generalized cover, 75, 100 Generating itemset, 11, 14, 17 Hidden-first algorithm, 33 Hiding candidate, Hiding goals, 13 Hiding side-effects ghost rules, 13 lost rules, 13 Hiding solution approximate, 13 exact, 13 feasible, 13, 14 optimal, 13, 98 Independent block, 66 Independent components, 123, 126 Inference, 4, 7, 9, 21, 22 Integer programming, 20, 63 Integer programming solver, 64 Inverse frequent itemset mining, 7, 143 Item ordering effect,

6 150 Index Itemset lattice, 41, 42 Knowledge hiding, 5, 8 Lattice, see Itemset lattice Limiting factor, 83 Max min criterion, 53, 55 Max min itemset, 53, 54 Minimal sensitive itemsets, 43, 49, 72 Modification strategy blocking, 5, 18, 35 distortion, 5, 13, 18, 29 Motivating examples Deadtrees & BigMart, 6 National Institutes of Health, 3 Wal-mart & Procter and Gamble, 3 Multiple rule hiding, 18, 30 Non-hidden first algorithm, 33 NP-hard, 64, 73, 74, 100 Null transaction, 105 Optimal borderline, 84 Optimal hiding scenario, 72 Optimal set, 43 Optimal solution, 98 Optimal solution set, 103 Optimization, viii, 20 Original border, 43 Oscillation, 85 Partitioning approach, 110 Priority-based distortion, 32 Privacy issues, 7 Privacy preserving clustering density-based approaches, 23 protocol-based approaches, 22, 23 transformation approaches, 22, 23 Privacy preserving data mining, vii, ix, 5 Problem instance (def), 83 Problem variants, 14, 15 Relative support, 47 50, 106 Relaxation process, see Constraints degree reduction Revised border, 43 Safety margin, 35, 36, 101, , 132 Sequence hiding, 23, 24 Single rule hiding, 18, 30, 31 Structural decomposition, 120 Taxonomy, 17, 19 Tentative victim item, 54 Unknowns, 18, 35 Validity of transactions, 106 Weight-based sorting distortion, 32

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