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Information-Theoretically Secure Privacy Preserving Approaches for Collaborative Association Rule Mining

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Abstract

In recent years, there is an increase in the geographical and logical spread of data. Even the organizations competing with each other normally, increasingly collaborate with each other to exploit the distributed data and collaboratively undertake data mining therein. However, the increased sharing of data gives rise to privacy concerns as the collaborative entities may be competing with each other. The need for efficient algorithms in terms of privacy and efficiency for the different adversary and data models for various areas of application is therefore an important research problem. In this chapter, we discuss the state-of-the-art of cryptographic Privacy Preserving Distributed Data Mining (PPDDM) approaches. In particular, we focus on the case study of Privacy Preservation in Distributed Association Rule Mining (PPDARM). We primarily discuss information-theoretically secure schemes that aim to improve the state-of-the-art in the area of PPDARM by providing the highest level of security.We discuss efficient and secure privacy preserving information-theoretically secure schemes that an application designer could choose from depending on the application requirements, the partition model, the adversary model and the number of participating parties for collaborative association rule mining.

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References

  1. Data. “Data everywhere,” The Economist, Feb 2010. [Online]. Available: http://www.economist.com/node/15557443. Accessed 13-January-2015.

  2. Fan, W., & Bifet, A. (2013). Mining big data: current status, and forecast to the future. ACM SIGKDD Explorations Newsletter, 14(2), 1–5.

    Article  Google Scholar 

  3. Seifert, J. W. (2013). CRS report for congress: data mining and homeland security an overview August 27, 2008 - RL31798. Mannheim, W. Germany, Germany: Bibliographisches Institut AG.

    Google Scholar 

  4. Aggarwal, C. C., & Yu, P. S. (2008). An introduction to privacy-preserving data mining. In Privacy-Preserving Data Mining Models and Algorithms, ser. Advances in Database Systems (vol. 34, pp. 1–9). New York: Springer US.

    Google Scholar 

  5. “9 important elements to corporate data security policies that protect data privacy,” The Security Magazine, may 2016, [Online]. Available: http://www.securitymagazine.com/articles/. Accessed 18-February-2017.

  6. Bachrach, D. G., & Rzeszut, E. J. (2014). Don’t Let the Snoops In. In 10 Don’ts on Your Digital Devices. Berkeley, CA: Apress.

    Google Scholar 

  7. Kantarcioglu, M., & Nix, R. (2010). Incentive compatible distributed data mining. In Second International Conference on Social Computing (SocialCom) (pp. 735–742). Minneapolis, Minnesota, USA: IEEE.

    Google Scholar 

  8. Du, W., & Atallah, M. J. (2001). Secure multi-party computation problems and their applications: a review and open problems. In Proceedings of the 2001 Workshop on New Security Paradigms, ser. NSPW ’01 (pp. 13–22). New York, NY, USA: ACM.

    Google Scholar 

  9. Lindell, Y., & Pinkas, B. (2000). Privacy preserving data mining. In Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology, ser. CRYPTO ’00 (pp. 36–54). London, UK, UK: Springer-Verlag.

    Google Scholar 

  10. Bogetoft, P., Christensen, D., Damgård, I., Geisler, M., Jakobsen, T., Krøigaard, M., et al. (2009). Secure multiparty computation goes live. In 13th International Conference on Financial Cryptography and Data Security, ser. Lecture Notes in Computer Science (vol. 5628, pp. 325–343). Accra Beach, Barbados: Springer/Berlin/Heidelberg.

    Google Scholar 

  11. Kantarcioglu, M., & Clifton, C. (2004). Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1026–1037.

    Article  Google Scholar 

  12. Kargupta, H., Das, K., & Liu, K. (2007). Multi-party, privacy-preserving distributed data mining using a game theoretic framework. In Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases, ser. PKDD (pp. 523–531). Berlin/Heidelberg: Springer-Verlag.

    Google Scholar 

  13. Sekhavat, Y., & Fathian, M. (2010). Mining frequent itemsets in the presence of malicious participants. IET Information Security, 4, 80–92.

    Article  Google Scholar 

  14. Kantarcioglu, M. (2008). A survey of privacy-preserving methods across horizontally partitioned data. In Privacy-Preserving Data Mining, ser. Advances in Database Systems (vol. 34, pp. 313–335). New York: Springer US.

    Google Scholar 

  15. Cheung, D. W., Han, J., Ng, V. T., Fu, A. W., & Fu, Y. (1996). A fast distributed algorithm for mining association rules. In Proceedings of the Fourth International Conference on Parallel and Distributed Information Systems, ser. DIS ’96 (pp. 31–43). Washington, DC, USA: IEEE Computer Society.

    Google Scholar 

  16. Wang, W., Deng, B., & Li, Z. (2007). Application of oblivious transfer protocol in distributed data mining with privacy-preserving. In Proceedings of the The First International Symposium on Data, Privacy, and E-Commerce (pp. 283–285). Washington, DC, USA: IEEE Computer Society.

    Google Scholar 

  17. Vaidya, J. (2008). A survey of privacy-preserving methods across vertically partitioned data. In Privacy-Preserving Data Mining, ser. The Kluwer International Series on Advances in Database Systems (vol. 34, pp. 337–358). New York: Springer US.

    Google Scholar 

  18. Samet, S., & Miri, A. (2009). Secure two and multi-party association rule mining. In Proceedings of the Second IEEE International Conference on Computational Intelligence for Security and Defense Applications, ser. CISDA’09 (pp. 297–302). Piscataway, NJ, USA: IEEE Press.

    Google Scholar 

  19. Vaidya, J., & Clifton, C. (2005). Secure set intersection cardinality with application to association rule mining. Journal of Computer Security, 13(4), 593–622.

    Article  Google Scholar 

  20. Ge, X., Yan, L., Zhu, J., & Shi, W. (2010). Privacy-preserving distributed association rule mining based on the secret sharing technique. In 2nd International Conference on Software Engineering and Data Mining (SEDM 2010) (pp. 345–350). Chengdu: IEEE.

    Google Scholar 

  21. Evfimievski, A., & Grandison, T. (2007). Privacy preserving data mining. San Jose, California: IBM Almaden Research Center.

    Google Scholar 

  22. Aggarwal, C. C., & Yu, P. S. (2008). A general survey of privacy-preserving data mining models and algorithms. In Privacy-Preserving Data Mining, ser. The Kluwer International Series on Advances in Database Systems (vol. 34, pp. 11–52). New York: Springer US.

    Google Scholar 

  23. Barthe, G., Grégoire, B., Heraud, S., & Zanella Béguelin, S. (2009). Formal certification of ElGamal encryption—A gentle introduction to CertiCrypt. In 5th International Workshop on Formal Aspects in Security and Trust, (FAST 2008), ser. Lecture Notes in Computer Science (vol. 5491, pp. 1–19). Malaga, Spain: Springer/Berlin/Heidelberg.

    Google Scholar 

  24. Pedersen, T. B., Saygin, Y., & Savas, E. (2007). Secret sharing vs. encryption-based techniques for privacy preserving data mining. Sciences-New York, 17–19.

    Google Scholar 

  25. Casey, E., & Rose, C. W. (2010). Chapter 2 - Forensic analysis. In Handbook of Digital Forensics and Investigation (pp. 21–47). San Diego: Academic Press.

    Chapter  Google Scholar 

  26. Wikipedia. (2014). Information-theoretic security — Wikipedia, The Free Encyclopedia.

    Google Scholar 

  27. Shamir, A. (1979). How to share a secret. Communication ACM, 22, 612–613.

    Article  MathSciNet  MATH  Google Scholar 

  28. Castelluccia, C., Chan, A. C.-F., Mykletun, E., & Tsudik, G. (2009) Efficient and provably secure aggregation of encrypted data in wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 5(3), 20:1–20:36.

    Google Scholar 

  29. Vetter, B., Ugus, O., Westhoff, D., & Sorge, C. (2012). Homomorphic primitives for a privacy-friendly smart metering architecture. In International Conference on Security and Cryptography (SECRYPT 2012), Rome, Itly (pp. 102–112).

    Google Scholar 

  30. Nanavati, N. R., Lalwani, P., & Jinwala, D. C. (2014). Analysis and evaluation of schemes for secure sum in collaborative frequent itemset mining across horizontally partitioned data. Journal of Engineering, 2014, p. 10.

    Article  Google Scholar 

  31. Nanavati, N. R., & Jinwala, D. C. (2012). Privacy preserving approaches for global cycle detections for cyclic association rules in distributed databases. In International Conference on Security and Cryptography (SECRYPT 2012) (pp. 368–371). Rome, Italy: SciTePress.

    Google Scholar 

  32. Nanavati, N. R., Sen, N., & Jinwala, D. C. (2014). Analysis and evaluation of efficient privacy preserving techniques for finding global cycles in temporal association rules across distributed databases. International Journal of Distributed Systems and Technologies (IJDST), 5(3), 58–76.

    Article  Google Scholar 

  33. Miyaji, A., & Rahman, M. (2011). Privacy-preserving data mining: a game-theoretic approach. In Proceedings of the 25th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, ser. Lecture Notes in Computer Science (vol. 6818, pp. 186–200). Richmond, VA, USA: Springer/Berlin/Heidelberg.

    Google Scholar 

  34. Nanavati, N. R., & Jinwala, D. C. (2013). A novel privacy preserving game theoretic repeated rational secret sharing scheme for distributed data mining. In Security and Privacy Symposium, IIT Kanpur, 2013. [Online]. Available: http://www.cse.iitk.ac.in/users/sps2013/submitting.html.

  35. Nanavati, N. R., & Jinwala, D. C. (2013). A game theory based repeated rational secret sharing scheme for privacy preserving distributed data mining. In 10th International Conference on Security and Cryptography (SECRYPT) (pp. 512–517), Reykjavik, Iceland. [Online]. Available: http://www.scitepress.org/DigitalLibrary/Index/DOI/10.5220/0004525205120517.

  36. Abraham, I., Dolev, D., Gonen, R., & Halpern, J. (2006). Distributed computing meets game theory: robust mechanisms for rational secret sharing and multiparty computation. In Proceedings of the Twenty-Fifth Annual ACM Symposium on Principles of Distributed Computing, ser. PODC ’06 (pp. 53–62). New York, NY, USA: ACM.

    Google Scholar 

  37. Halpern, J., & Teague, V. (2004). Rational secret sharing and multiparty computation: extended abstract. In Proceedings of the Thirty-Sixth Annual ACM Symposium on Theory of Computing, ser. STOC ’04 (pp. 623–632). New York, NY, USA: ACM.

    Google Scholar 

  38. Maleka, S., Shareef, A., & Rangan, C. (2008). Rational secret sharing with repeated games. In 4th International Conference on Information Security Practice and Experience (ISPEC), ser. Lecture Notes in Computer Science (vol. 4991, pp. 334–346). Sydney, Australia: Springer/Berlin/Heidelberg.

    Google Scholar 

  39. Nanavati, N. R., & Jinwala, D. C. (2012). Privacy preservation for global cyclic associations in distributed databases. Procedia Technology, 6(0), 962–969. In 2nd International Conference on Communication, Computing and Security [ICCCS-2012].

    Google Scholar 

  40. Nanavati, N. R., Lalwani, P., & Jinwala, D. C. (2014). Novel game theoretic privacy preserving construction for rational and malicious secret sharing models for collaborative frequent itemset mining. Journal of Information Security and Applications (JISA). Submitted for consideration in Sep-2016.

    Google Scholar 

  41. Vaidya, J. S. (2004). Privacy preserving data mining over vertically partitioned data (Ph.D. dissertation, Centre for Education and Research in Information Assurance and Security, Purdue, West Lafayette, IN, USA, Aug 2004), aAI3154746. [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.4249.

  42. Keshavamurthy, B. N., Khan, A., & Toshniwal, D. (2013). Privacy preserving association rule mining over distributed databases using genetic algorithm. Neural Computing and Applications, 22(Supplement-1), 351–364. [Online]. Available: http://dx.doi.org/10.1007/s00521-013-1343-9.

  43. Du, W., & Atallah, M. (2001). Protocols for secure remote database access with approximate matching. In E-Commerce Security and Privacy, ser. Advances in Information Security (vol. 2, pp. 87–111). New York: Springer US.

    Google Scholar 

  44. Bogdanov, D., Jagomägis, R., & Laur, S. (2012). A universal toolkit for cryptographically secure privacy-preserving data mining. In Proceedings of the 2012 Pacific Asia Conference on Intelligence and Security Informatics, ser. PAISI’12 (pp. 112–126). Berlin/Heidelberg: Springer-Verlag.

    Google Scholar 

  45. Nanavati, N. R., & Jinwala, D. C. (2015). A novel privacy-preserving scheme for collaborative frequent itemset mining across vertically partitioned data. Security and Communication Networks, 8(18), 4407–4420.

    Article  Google Scholar 

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Authors and Affiliations

  1. Sarvajanik College of Engineering and Technology (SCET), Surat, India

    Nirali R. Nanavati

  2. S. V. National Institute of Technology (SVNIT), Surat, India

    Devesh C. Jinwala

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  1. Nirali R. Nanavati
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  2. Devesh C. Jinwala
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Correspondence to Nirali R. Nanavati .

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  1. University of Detroit Mercy, Detroit, Michigan, USA

    Kevin Daimi

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Nanavati, N.R., Jinwala, D.C. (2018). Information-Theoretically Secure Privacy Preserving Approaches for Collaborative Association Rule Mining. In: Daimi, K. (eds) Computer and Network Security Essentials. Springer, Cham. https://doi.org/10.1007/978-3-319-58424-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-58424-9_4

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