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Privacy in Spatiotemporal Data Mining

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Mobility, Data Mining and Privacy

Privacy is an essential requirement for the provision of electronic and knowledgebased services in modern e-business, e-commerce, e-government, and e-health environments. Nowadays, service providers can easily track individuals’ actions, behaviors, and habits. Given large data collections of person-specific information, providers can mine data to learn patterns, models, and trends that can be used to provide personalized services. The potential benefits of data mining are substantial, but it is evident that the collection and analysis of sensitive personal data arouses concerns about citizens’ privacy, confidentiality, and freedom.

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References

  1. N.R. Adam and J.C. Wortmann. Security-control methods for statistical databases: A comparative study. ACM Computing Surveys, 21(4):515–556, 1989.

    Article  Google Scholar 

  2. C.C. Aggarwal. On k-anonymity and the curse of dimensionality. In Proceedings of the 31th International Conference on Very Large Databases (VLDB’05), pp. 901–909, 2005.

    Google Scholar 

  3. D. Agrawal and C.C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In Proceedings of the 20th Symposium on Principles of Database Systems (PODS’01), pp. 247–255, 2001.

    Google Scholar 

  4. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of International Conference on Management of Data (SIGMOD’93), pp. 207–216, 1993.

    Google Scholar 

  5. 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’94), pp. 487–499, 1994.

    Google Scholar 

  6. R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proceedings of International Conference on Management of Data (SIGMOD’00), pp. 439–450, 2000.

    Google Scholar 

  7. S. Agrawal and J.R. Haritsa. A framework for high-accuracy privacy-preserving mining. In Proceedings of the 21st International Conference on Data Engineering (ICDE’05), pp. 193–204, 2005.

    Google Scholar 

  8. M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim, and V.S. Verykios. Disclosure limitation of sensitive rules. In Proceedings of the Knowledge and Data Engineering Exchange Workshop (KDEX’99), pp. 45–52, 1999.

    Google Scholar 

  9. M. Atzori. Weak k-anonymity: A low-distortion model for protecting privacy. In Proceedings of the 8th International Information Security Conference (ISC06), pp. 60–71, 2006.

    Google Scholar 

  10. M. Atzori, F. Bonchi, F. Giannotti, and D. Pedreschi. Blocking anonymity threats raised by frequent itemset mining. In Proceedings of the 5th International Conference on Data Mining (ICDM’05), pp. 561–564, 2005.

    Google Scholar 

  11. M. Atzori, F. Bonchi, F. Giannotti, and D. Pedreschi. k-Anonymous patterns. In Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’05), pp. 10–21, 2005.

    Google Scholar 

  12. M. Atzori, F. Bonchi, F. Giannotti, and D. Pedreschi. Anonymity preserving pattern discovery. Very Large Data Bases Journal. To Appear.

    Google Scholar 

  13. E. Bertino, I.N. Fovino, and L.P. Povenza. A framework for evaluating privacy preserving data mining algorithms. Data Mining and Knowledge Discovery, 11(2):121–154, 2005.

    Article  MathSciNet  Google Scholar 

  14. C. Bettini and S. Mascetti. Preserving k-anonymity in spatiotemporal datasets and location-based services. In First Italian Workshop on PRIvacy and SEcurity (PRISE), 2006.

    Google Scholar 

  15. R. Canetti, U. Feige, O. Goldreich, and M. Naor. Adaptively secure multi-party computation. In Proceedings of the 28th Annual Symposium on Theory of Computing (STOC’96), pp. 639–648. ACM Press, 1996.

    Google Scholar 

  16. L. Chang and I.S. Moskowitz. Parsimonious downgrading and decision trees applied to the inference problem. In Proceedings of the Workshop on New Security Paradigms (NSPW’98), pp. 82–89, 1998.

    Google Scholar 

  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, pp. 47–56, 2004.

    Google Scholar 

  18. C. Clifton. Using sample size to limit exposure to data mining. Journal of Computer Security, 8(4):281–307, 2000.

    Google Scholar 

  19. C. Clifton, M. Kantarcioglu, and J. Vaidya. Defining privacy for data mining. In Natural Science Foundation Workshop on Next Generation Data Mining, pp. 126–133, 2002.

    Google Scholar 

  20. C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M.Y. Zhu. Tools for privacy preserving distributed data mining. ACM SIGKDD Exploration Newsletter, 4(2):28–34, 2002.

    Article  Google Scholar 

  21. C. Clifton and D. Marks. Security and privacy implications of data mining. In Proceedings of International Conference on Management of Data (SIGMOD’96), pp. 15–19, 1996.

    Google Scholar 

  22. 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 (HI’01), pp. 369–383, 2001.

    Google Scholar 

  23. W. Diffie and M.E. Hellman. New directions in cryptography. IEEE Transactions on Information Theory, IT-22(6):644–654, 1976.

    Article  MathSciNet  Google Scholar 

  24. W. Du and M.J. Atallah. Privacy-preserving statistical analysis. In Proceedings of the 17th Annual Computer Security Applications Conference (ACSAC’01), pp. 102–110, 2001.

    Google Scholar 

  25. W. Du and Z. Zhan. Building decision tree classifier on private data. In Proceedings of the International Conference on Privacy, Security and Data Mining (CRPITS’02), pp. 1–8, 2002.

    Google Scholar 

  26. W. Du and Z. Zhan. Using randomized response techniques for privacy-preserving data mining. In Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining (KDD’03), pp. 505–510, 2003.

    Google Scholar 

  27. M. Duckham and L. Kulik. A formal model of obfuscation and negotiation for location privacy. In Proceedings of the Third International Conference on Pervasive Computing (Pervasive’05), pp. 152–170, 2005.

    Google Scholar 

  28. C. Dwork and K. Nissim. Privacy preserving data mining in vertically partitioned databases. In Proceedings of the 24th International Conference on Cryptology (CRYPTO’04), pp. 528–544, 2004.

    Google Scholar 

  29. T. ElGamal. A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Transactions Information Theory, 31:469–472, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  30. F. Emekci, O.D. Sahin, D. Agrawal and A. El Abbadi. Privacy preserving decision tree learning over multiple parties. Data & Knowledge Engineering. 63(2):348–361, 2007.

    Article  Google Scholar 

  31. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke. Privacy preserving mining of association rules. In Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining (KDD’02), pp. 343–364, 2002.

    Google Scholar 

  32. M. Fischlin. A cost-effective pay-per-multiplication comparison method for millionaires. Lecture Notes in Computer Science, 2020:457, 2001.

    Article  MathSciNet  Google Scholar 

  33. A. Friedman, A. Schuster, and R. Wolff. k-Anonymous decision tree induction. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’06), pp. 151–162. Springer-Verlag, 2006.

    Google Scholar 

  34. P. Fule and J.F. Roddick. Detecting privacy and ethical sensitivity in data mining results. In Proceedings of the 22nd Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalisation, pp. 159–166, 2004.

    Google Scholar 

  35. B. Gedik and L. Liu. Location privacy in mobile systems: A personalized anonymization model. In Proceedings of the 25th International Conference on Distributed Computing Systems (ICDCS’05), pp. 620–629, 2005.

    Google Scholar 

  36. O. Goldreich, S. Micali, and A. Wigderson. How to play any mental game or a completeness theorem for protocols with honest majority. In Proceedings of 19th Annual Symposium on Theory of Computing (STOC’87), pp. 218–229, 1987.

    Google Scholar 

  37. B. Hoh and M. Gruteser. Location privacy through path confusion. In Proceedings of IEEE/CreateNet International Conference on Security and Privacy for Emerging Areas in Communication Networks (SecureComm’05), 2005.

    Google Scholar 

  38. S.-Y. Hwang, Y.-H. Liu, J.-K. Chiu, and E.-P. Lim. Mining mobile group patterns: A trajectory-based approach. In Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’05), pp. 713–718, 2005.

    Google Scholar 

  39. A. Inan and Y. Saygin. Privacy-preserving spatio-temporal clustering on horizontally partitioned data. In Proceedings of 8th International Conference on Data Warehousing and Knowledge Discovery (DaWaK’06), Vol. 4081. Lecture Notes in Computer Science, pp. 459–468. Springer, 2006.

    Google Scholar 

  40. W. Jiang and M. Atzori. Secure distributed k-anonymous pattern mining. In Proceedings of the 6th International Conference on Data Mining (ICDM’06). pp. 319–329.

    Google Scholar 

  41. T. Johnsten and V.V. Raghavan. Impact of decision-region based classification mining algorithms on database security. In Proceedings of the IFIP TC13 WG11.3 13th International Conference on Database Security, pp. 177–191, 2000.

    Google Scholar 

  42. M. Kantarcioglu and C. Clifton. Privacy-preserving distributed mining of association rules on horizontally partitioned data. In In The ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD’02), 2002.

    Google Scholar 

  43. M. Kantarcioglu, J. Jin, and C. Clifton. When do data mining results violate privacy? In Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining (KDD’04), pp. 599–604, 2004.

    Google Scholar 

  44. H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. On the privacy preserving properties of random data perturbation techniques. In Proceedings of the 3rd International Conference on Data Mining (ICDM’03), pp. 99, 2003.

    Google Scholar 

  45. H. Kido. Location Anonymization for Protecting User Privacy in Location-Based Services. MS Thesis. 2006.

    Google Scholar 

  46. D. Kifer and J. Gehrke. Injecting utility into anonymized datasets. In Proceedings of International Conference on Management of Data (SIGMOD’06), pp. 217–228, 2006.

    Google Scholar 

  47. M. Klusch, S. Lodi, and G. Moro. Distributed clustering based on sampling local density estimates. In Proceedings of Internatational Joint Conference on Artificial Intelligence, pp. 485–490, 2003.

    Google Scholar 

  48. G. Lee, C.-Y. Chang, and A.L.P. Chen. Hiding sensitive patterns in association rules mining. In Proceedings of 28th Annual International Computer Software and Applications Conference (COMPSAC’04), pp. 424–429, 2004.

    Google Scholar 

  49. H.Y. Lin and W.G. Tzeng. An efficient solution to the millionaires’ problem based on homomorphic encryption. In Proceedings of Third International Conference on Applied Cryptography and Network Security (ACNS’05), Vol. 3531. Lecture Notes in Computer Science, pp. 456–466, 2005.

    Google Scholar 

  50. X. Lin, C. Clifton, and M. Zhu. Privacy preserving clustering with distributed EM mixture modeling. Knowledge and Information Systems, 8:68–81, 2005.

    Article  Google Scholar 

  51. Y. Lindell and B. Pinkas. Privacy preserving data mining. Lecture Notes in Computer Science, 1880:36–52, 2000.

    Article  MathSciNet  Google Scholar 

  52. K. Liu, H. Kargupta, and J. Ryan. Random projection-based multiplicative perturbation for privacy preserving distributed data mining. IEEE Transactions on Knowledge and Data Engineering, 18(1):92–106, 2006.

    Article  Google Scholar 

  53. H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1(3):241–258, 1997.

    Article  Google Scholar 

  54. S. Mascetti, C. Bettini, X.S. Wang, and S. Jajodia. k-Anonymity in databases with timestamped data. In Proceedings of the Thirteenth International Symposium on Temporal Representation and Reasoning (TIME’06), pp. 177–186. IEEE Computer Society, 2006.

    Google Scholar 

  55. S. Menon, S. Sarkar, and S. Mukherjee. Maximizing accuracy of shared databases when concealing sensitive patterns. Information Systems Research, 16(3):256–270, 2005.

    Article  Google Scholar 

  56. S. Merugu and J. Ghosh. Privacy-preserving distributed clustering using generative models. In Proceedings of the 3rd International Conference on Data Mining (ICDM’03), p. 211. IEEE Computer Society, 2003.

    Google Scholar 

  57. M. Morgenstern. Controlling logical inference in multilevel database and knowledge-base systems. In Proceedings of the Symposium on Security and Privacy, pp. 245–255. IEEE, 1988.

    Google Scholar 

  58. 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’05), pp. 468–477, 2005.

    Google Scholar 

  59. J. Natwichai, X. Li, and M. Orlowska. A reconstruction-based algorithm for classiciation rules hiding. In Proceedings of the 17th Australasian Database Conference (ADC’06), pp. 49–58, 2006.

    Google Scholar 

  60. D.E. O’Leary. Knowledge discovery as a threat to database security. In G. Piatetsky-Shapiro and W.J. Frawley (eds.), Knowledge Discovery in Databases, pp. 507–516. AAAI/MIT Press, 1991.

    Google Scholar 

  61. S. Oliveira and O. Zaiane. Privacy preserving clustering by object similarity-based representation. In Proceedings of the Workshop on Privacy and Security Aspects of Data Mining, pp. 40–46, 2004.

    Google Scholar 

  62. S.R.M. Oliveira and O.R. Zaiane. A Framework for Enforcing Privacy in Mining Frequent Patterns. Technical report, Computer Science Department, University of Alberta, 2002.

    Google Scholar 

  63. S.R.M. Oliveira and O.R. Zaïane. Privacy preserving frequent itemset mining. In Proceedings of the International Conference on Privacy, Security and Data Mining (CRPITS’02), pp. 43–54, 2002.

    Google Scholar 

  64. S.R.M. Oliveira and O.R. Zaiane. Algorithms for balancing privacy and knowledge discovery in association rule mining. In Proceedings of the International Database Engineering and Applications Symposium (IDEAS’03), pp. 54–63, 2003.

    Google Scholar 

  65. S.R.M. Oliveira and O.R. Zaïane. Protecting sensitive knowledge by data sanitization. In Proceedings of the 3rd International Conference on Data Mining (ICDM’03), pp. 211–218, 2003.

    Google Scholar 

  66. S.R.M. Oliveira and O.R. Zaiane. A unified framework for protecting sensitive association rules in business collaboration. International Journal of Business Intelligence and Data Mining, 1(3):247–287, 2006.

    Google Scholar 

  67. S.R.M. Oliveira, O.R. Zaïane, and Y. Saygin. Secure association rule sharing. In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’04), pp. 74–85, 2004.

    Google Scholar 

  68. P. Paillier. Public-key cryptosystems based on composite degree residuosity classes. Lecture Notes in Computer Science, 1592:223–238, 1999.

    Article  MathSciNet  Google Scholar 

  69. G. Piatetsky-Shapiro, U.M. Fayyad and P. Smyth. From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining, pp. 1–34. AAAI Press, 1996.

    Google Scholar 

  70. 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’04), pp. 325–339, 2004.

    Google Scholar 

  71. E.D. Pontikakis, V.S. Verykios, and Y. Theodoridis. On the comparison of association rule hiding heuristics. In Hellenic Database Management Symposium, 2004.

    Google Scholar 

  72. 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’02), 2002.

    Google Scholar 

  73. P. Samarati and L. Sweeney. Generalizing Data to Provide Anonymity When Disclosing Information. Technical report, 1998. Available at http://www.sld.sri.com/papers/344/.

  74. Y. Saygin, V.S. Verykios, and C. Clifton. Using unknowns to prevent discovery of association rules. ACM SIGMOD Record, 30(4):45–54, 2001.

    Article  Google Scholar 

  75. Y. Saygin, V.S. Verykios, and A.K. Elmagarmid. Privacy preserving association rule mining. In Proceedings of the International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems (RIDE’02), 2002.

    Google Scholar 

  76. X. Sun and P.S. Yu. A border-based approach for hiding sensitive frequent itemsets. In Proceedings of the 5th International Conference on Data Mining (ICDM’05), pp. 426–433, 2005.

    Google Scholar 

  77. L. Sweeney. Datafly: A system for providing anonymity in medical data. In Proceedings of the IFIP TC11 WG11.3 11th International Conference on Database Security, pp. 356–381, 1998.

    Google Scholar 

  78. L. Sweeney. k-Anonymity: A model for protecting privacy. International Journal on Uncertainty Fuzziness and Knowledge-based Systems, 10(5), 2002.

    Google Scholar 

  79. J. Vaidya and C. Clifton. Privacy preserving association rule mining in vertically partitioned data. In Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining (KDD’02), pp. 639–644, 2002.

    Google Scholar 

  80. J. Vaidya and C. Clifton. Privacy-preserving k-means clustering over vertically partitioned data. In Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining (KDD’03), pp. 206–215, 2003.

    Google Scholar 

  81. J. Vaidya and C. Clifton. Privacy preserving naïve bayes classifier for vertically partitioned data. In Proceedings of the International Conference on Data Mining (SDM’04), 2004.

    Google Scholar 

  82. 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, 2004.

    Article  Google Scholar 

  83. 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):434–447, 2004.

    Article  Google Scholar 

  84. E.T. Wang, G. Lee, and Y.T. Lin. A novel method for protecting sensitive knowledge in association rules mining. In Proceedings of 29th Annual International Computer Software and Applications Conference (COMPSAC’05), pp. 511–516, 2005.

    Google Scholar 

  85. K. Wang, B.C.M. Fung, and P.S. Yu. Template-based privacy preservation in classification problems. In Proceedings of the 5th International Conference on Data Mining (ICDM’05), pp. 466–473, 2005.

    Google Scholar 

  86. S. Warner. Randomized response: A survey technique for eliminating evasive answer bias. Journal of The American Statistical Association, 60(309), 1965.

    Google Scholar 

  87. X. Xiao and Y. Tao. Anatomy: Simple and effective privacy preservation. In Proceedings of the 32th International Conference on Very Large Databases (VLDB’06), 2006.

    Google Scholar 

  88. Z. Yang, S. Zhong, and R.N. Wright. Privacy-preserving classification of customer data without loss of accuracy. In The 2005 SIAM International Conference on Data Mining (SDM’05), 2005.

    Google Scholar 

  89. A.C. Yao. Protocols for secure computations. In Proceedings of 23th Annual Symposium on Foundations of Computer Science (FOCS’82), pp. 160–164. IEEE Computer Society, 1982.

    Google Scholar 

  90. J. Zhan and S. Matwin. Privacy-preserving data mining in electronic surveys. International Journal of Network Security, 4(3):318–327, 2007.

    Google Scholar 

  91. J. Zhan, S. Matwin, and L. Chang. Privacy-preserving collaborative association rule mining. In Proceedings of the 19th Annual IFIP Conference on Data and Applications Security (DBSEC’05), Vol. 3654. Lecture Notes in Computer Science, pp. 153–165, 2005.

    Google Scholar 

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Authors and Affiliations

  1. KDD Laboratory, ISTI-CNR, Pisa, Italy

    F. Bonchi & M. Atzori

  2. Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey

    Y. Saygin, S. V. Kaya & E. Savaş

  3. Department of Computer and Communication Engineering, University of Thessaly, Volos, Greece

    V. S. Verykios & A. Gkoulalas-Divanis

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  1. F. Bonchi
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  1. KDD Laboratory ISTI-CNR, Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, Via G. Moruzzi, 1, 56124, Pisa, Italy

    Fosca Giannotti

  2. KDD Laboratory Dipartimento di Informatica, Università di Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy

    Dino Pedreschi

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Bonchi, F. et al. (2008). Privacy in Spatiotemporal Data Mining. In: Giannotti, F., Pedreschi, D. (eds) Mobility, Data Mining and Privacy. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75177-9_12

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