Bibliografia
[1] R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. of VLDB 1994, pages 487-499, 1994.
[2] B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In Proc. of the 4th Int.‘l Conference on Knowledge Discovery and DataMining (KDD1998), pages
80-86, 1998.
[3] X. Yin and J. Han. CPAR: Classification based on Predictive Association Rules. In Proc. of the 3rd SIA International Conference on Data Mining, 2003.
[4] J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.
[5] C. S. Peirce. Collected papers of Charles Sanders Peirce. Vol.2, Hartshorn et al. eds., Harvard Univesity Press.
[6] J. Rauch. Logic of association rules. Appl. Intell., 22(1): 9-28, 2005.
[7] B.Goethals. Frequent Itemset Mining Implementations Repository., http://fimi.cs.helsinki.fi .
[8] R. Srikant and R. Agrawal. Mining generalized association rules. In VLDB’95, Proceedings of 21th International
Conference on Very Large Data Bases, pages 407-419.
MorganKaufmann,1995.
[9] http://www.parlamento.it/parlam/leggi .
[10] Putting Equality Into Practice. 2006. http://www.stop-discrimination.info/942.0.html .
[11] Combating Discrimination - A Training Manual. 2006. http://www.stop-discrimination.info/942.0.html .
[12] European Anti-Discrimination Law Review IV. 2006. http://www.stop-discrimination.info/942.0.html .
[13] William W. Cohen. Fast effective rule induction. In
ICML, pages 115–123, 1995.
[14] Pavel Berkhin. Survey of clustering data mining techniques. Technical report, Accrue Software, San Jose, CA, 2002.
[15] Equality and non-discrimination. Annual report 2006. http://www.stop-discrimination.info/942.0.html .
[16] Rakesh Agrawal, Tomasz Imielinski, and Arun N. Swami. Mining association rules between sets of items in large databases. In SIGMOD Conference, pages 207–216, 1993.
[17] Dakshi Agrawal and Charu C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In Symposium on Principles of Database Systems, 2001.
[18] Maurizio Atzori, Francesco Bonchi, Fosca Giannotti, and Dino Pedreschi. Anonymity and data mining. Computer
Systems: Science & Engineering, 20(5): 369–376, 2005.
[19] Maurizio Atzori, Paolo Mancarella, and Franco Turini. Abduction in classification tasks. In A. Cappelli and F. Turini, editors, AI*IA 2003: Advances in Artificial Intelligence,
volume 2829 of LNAI, pages 213–224. SV, 2003.
[20] Rakesh Agrawal and Ramakrishnan Srikant. Privacy-preserving data mining. In Proceedings of the 2000 ACM
SIGMOD on Management of Data, pages 439–450, 2000.
[21] H. Christiansen. Abduction and induction combined in a metalogic framework. In A. Kakas P. Flach, editor, Abductive and Inductive Reasoning: Essays on their Relation and Integration, 2000.
[22] Chris Clifton, Murat Kantarcioglu, and Jaideep Vaidya. Defining privacy for data mining. In Natural Science Foundation Workshop on Next Generation Data Mining, 2002.
[23] Chris Clifton, Murat Kantarcioglu, Jaideep Vaidya, Xiaodong Lin, and Michael Y. Zhu. Tools for privacy preserving distributed data mining. SIGKDD Explor. Newsl., 4(2): 28–34, 2002.
[24] U. Fayyad, G. Piatetsky – Shapiro, P. Smyth. From Data Mining To Knowledge Discovery: An Overview. In U. Fayyad, G. Piatetsky – Shapiro, R. Uthurusamy (editori) Advances in Knowledge Discovery and Data Mining, AAAI Press / The MIT Press, 1996.
[25] Anwar M.N. (1993). Microaggregation: the small aggregates method. Internal Report, Eurostat.
[26] Race Relation Act 1976. U.K. Legislation, http://www.statutelaw.gov.uk
[27] Defays D., Anwar M.N. (1998). Masking Microdata Using Micro-aggregation. Journal of Official Statistics, vol. 14, n. 4.
[28] Smith, P.j. Effect of Addition of Random Error to Perturb Sensitive Data and to Preserve Confidentiality of Response. Internal memorandum, Census Bureau.
[29] Adam N.R. and Wortmann J.C., 1989. Security-control methods for statistical databases: A comparative study. ACM
[30] Sex Discrimination Act 1975. U.K. Legislation, http://www.statutelaw.gov.uk .
[31] Kim J., 1986. A method for limiting disclosure in microdata based on random noise and transformation. In
Proceedings of the American Statistical Association on Survey Research Methods, American Statistical Association,
Washington, DC, 370 - 374.
[32] Equal Opportunity Act 1995. Victoria State Legislation. http://www.austlii.edu.au .
[33] Thematic Study on Policy Measures concerning
Disadvantaged Youth.
http://www.stop-discrimination.info/942.0.html .
[34] Mettere in pratica la parità - Il ruolo dell’azione positiva. http://www.stop-discrimination.info/942.0.html .
[35] Laura Castelvetri. Discriminazione diretta e indiretta.
Donne Lavoro Società Crescere per Contare. 2006.
[36] Burridge Jim (2003). Information preserving statistical obfuscation. Journal of Official Statistics, 13, 321 - 327.
[37] Michel Miné. I concetti di discriminazione diretta e indiretta. Lotta contro la Discriminazione: le Nuove Direttive
[38] Dalenius Tore and Reiss Steven P. (1978). Data-swapping: A technique for disclosure control (extended abstract). American Statistical Association, Proceedings of the
Section on Survey Research Methods, Washington, DC, 191 -
194.
[39] J. Domingo-Ferrer and V.Torra, Ordinal, continuous and heterogenerous k-anonymity through microaggregation.
Data Mining and Knowledge Discovery, vol.11, no. 2, 2005.
[40] J. Domingo-Ferrer, A. Solanas and A. Martinez-Balleste. Privacy in Statistical Databases: k-Anonymity Through Microaggregation. IEEE, 2006.
[41] Intentional Employment Discrimination. U.S. Federal Legislation, http://www.usdoj.gov .
[42] L. Sweeney. Achieving k-anonymity privacy protection using generalization and suppression. International Journal of
Uncertainty, Fuzziness and Knowledge Based Systems, 10 (5):
571 - 588, 2002.
[43] L. Sweeney. K-anonimity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and
Knowledge Based Systems, 10 (5): 557 - 570, 2002.
[44] Murat Kantarcioglu, Jiashun Jin, and Chris Clifton. When do data mining results violate privacy? In Proceedings
[45] W. Du and Z. Zhan. Building decision tree classifier on private data. In C. Clifton and V. Estivill-Castro, editors, IEEE
International Conference on Data Mining Workshop on Privacy, Security, and Data Mining, volume 14, pages 1 – 8,
Maebashi City, Japan, Dec. 9 2002. Australian Computer Society.
[46] C. Farkas and S. Jajodia. The inference problem: A survey. SIGKDD Explorations, 4 (2): 6 – 11, Jan. 2003.
[47] M. Kantarcioglu and C. Clifton. Privacy-preserving distributed mining of association rules on horizontally partitioned data. In The ACM SIGMOD Workshop on
Research Issues on Data Mining and Knowledge Discovery (DMKD’02), 2002.
[48] C. Clifton and D. Marks. Security and Privacy Implications of Data Mining. In Workshop on Data Mining
and Knowledge Discovery, pages 15-19, 1996.
[49] Equal Pay Act. U.S. Federal Legislation, http://www.usdoj.gov .
[50] S. D. Warren and L. D. Brandeis. The Right to Privacy.
Harvard Law Review, 4(5): 193-220, 1890.
[51] Pregnancy Discrimination Act. U.S. Federal Legislation, http://www.usdoj.gov .
[52] Meyerson A. and R. Williams (2004). On the complexity of optimal k-anonymity. In PODS’2004, Paris,
France, 223-228.
[53] Fair Housing Act. U.S. Federal Legislation, http://www.usdoj.gov .
[54] Y. Lindell and B. Pinkas. Privacy preserving data mining. J. of Cryptology, 15: 177 – 206, 2002.
[55] Jaideep Shrikant Vaidya and Chris Clifton. Privacy preserving association rule mining in vertically partitioned data. Submitted to The Eighth ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, July
23-26 2002.
[56] L. Console, D. Dupré and P. Torasso. 1991. On the relationship between abduction and deduction. Journal of
Logic and Computation 1, 5, 661 – 690.
[57] Equal Credit Opportunity Act. U.S. Federal Legislation, http://www.usdoj.gov .
[58] Dino Pedreschi, Salvatore Ruggieri, Franco Turini. Discrimination-aware data mining. 2007.
[59] M. A. Palley andJ. S. Simonoff. 1987. The use of regression methodology for compromise of confidential information in statistical databases. ACM Trans. Database
[60] S. P. Reiss. 1984. Practical data swapping: The first step. ACM Trans. Database Syst. 9, 1 (Mar.), 20 – 37.
[61] G. Sande. 1983. Automated cell suppression to reserve confidentiality of business statistics. In Proceedings of the 2nd
International Workshop on Statistical Database Management,
346 – 353.
[62] Machanavajjhala A., Gehrke J., Kifer D. (2006). l-diversity: Privacy beyond k-anonymity. In Proc. of the
International Conference on Data Engineering (ICDE06),
Atlanta, GA, USA.
[63] Samarati P., Sweeney L. (1998). Generalizing data to provide anonymity when disclosing information (Abstract). In
Proc. of the 17th ACM – SIGMOD -SIGACT-SIGART Symposium on the Principles of Database Systems, p.188,
Seattle, WA, USA.
[64] V. Ciriani, S. De Capitani di Vimercati, S. Foresti and P. Samarati. K-anonymity. Università degli Studi di Milano.
[65] Traian Marius Truta, Bindu Vinay. Privacy Protection: p-Sensitive k-Anonymity Property .
[66] The platform for privacy preferences 1.0 (p3p1.0) specication, April 16 2002. URL http://www.w3.org/TR/P3P/ .
[67] Privacy bird, July 2002. URL http://www.privacybird.com .
[68] Shariq J. Rizvi and Jayant R. Haritsa. Maintaining data privacy in association rule mining. In Proceedings of 28th
International Conference on Very Large Data Bases. VLDB,
August 20-23 2002. URL http://www.vldb.org.
[69] Atzori, Bonchi, Giannotti, Pedreschi. K-anonymous patterns. In PKDD and ICDM. 2005.
[70] Quinlan, J.R. (1986). Induction of Decision Trees.
Machine Learning 1:1, 81-106.
[71] P. Fule and J. F. Roddick.. Detecting privacy and ethical sensitivity in data mining results. In Proc. of the 27°
conference on Australasian computer science, 2004.
[72] S. R. M. Oliveira, O. R. Zaiane, and Y. Saygin. Secure association rule sharing. In Proc. of the 8th PAKDD, 2004.
[73] B. Pinkas. Cryptographic techniques for privacy-preserving data mining. SIGKDD Explor. Newsl., 4(2), 2002.
[74] W. Kloesgen. Knowledge discovery in databases and data privacy. In IEEE Expert Symposium: Knowledge
Discovery in Databases, 1995.
support. In Proceedings of the 4th International Workshop on
Information Hiding, 2001.
[76] Y. Saygin, V. S. Verykios, and C. Clifton. Using unknowns to prevent discovery of association rules. SIGMOD Rec., 30(4), 2001.
[77] S. R. M. Oliveira and O. R. Zaiane. Protecting sensitive knowledge by data sanitization. In Third IEEE International
Conference on Data Mining (ICDM’03), 2003.
[78] W. Duand Z. Zhan. Using randomized response techniques for privacy- preserving data mining. In
Proceedings of SIGKDD. 2003.
[79] A. Evfimievski. Randomization in privacy preserving data mining. SIGKDD Explor. Newsl., 4(2), 2002.
[80] A. Evfimievski, J.Gehrke, and R. Srikant. Limiting privacy breaches in privacy preserving data mining. In
Proceedings of PODS . 2003.
[81] A. Evfimievski, R. Srikant, R. Agrawal, and J.Gehrke. Privacy preserving mining of association rules. In