Abstract
The issue of potential data misuse rises whenever it is collected from several sources. In a common setting, a large database is either horizontally or vertically partitioned between multiple entities who want to find global trends from the data. Such tasks can be solved with secure multi-party computation (MPC) techniques. However, practitioners tend to consider such solutions inefficient. Furthermore, there are no established tools for applying secure multi-party computation in real-world applications. In this paper, we describe Sharemind—a toolkit, which allows data mining specialist with no cryptographic expertise to develop data mining algorithms with good security guarantees. We list the building blocks needed to deploy a privacy-preserving data mining application and explain the design decisions that make Sharemind applications efficient in practice. To validate the practical feasibility of our approach, we implemented and benchmarked four algorithms for frequent itemset mining.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. of VLDB 1994, pp. 487–499. Morgan Kaufmann (1994)
Agrawal, S., Haritsa, J.R., Prakash, B.A.: FRAPP: a framework for high-accuracy privacy-preserving mining. Knowledge Discovery and Data Mining 18(1), 101–139 (2009)
Bogdanov, D., Laur, S., Willemson, J.: Sharemind: A Framework for Fast Privacy-Preserving Computations. In: Jajodia, S., Lopez, J. (eds.) ESORICS 2008. LNCS, vol. 5283, pp. 192–206. Springer, Heidelberg (2008)
Bogetoft, P., Damgård, I., Jakobsen, T., Nielsen, K., Pagter, J., Toft, T.: A Practical Implementation of Secure Auctions Based on Multiparty Integer Computation. In: Di Crescenzo, G., Rubin, A. (eds.) FC 2006. LNCS, vol. 4107, pp. 142–147. Springer, Heidelberg (2006)
Bramer, M.: Principles of Data Mining. Springer (2007)
Brijs, T., Swinnen, G., Vanhoof, K., Wets, G.: Using association rules for product assortment decisions: A case study. In: Proc. of KDD 1999, pp. 254–260. ACM (1999)
Burkhart, M., Strasser, M., Many, D., Dimitropoulos, X.: SEPIA: privacy-preserving aggregation of multi-domain network events and statistics. In: Proc. of USENIX Security 2010, p. 15. USENIX Association (2010)
Chor, B., Kushilevitz, E.: A zero-one law for boolean privacy. In: Proc. of STOC 1989, pp. 62–72. ACM Press (1989)
Damgård, I., Ishai, Y.: Constant-Round Multiparty Computation Using a Black-Box Pseudorandom Generator. In: Shoup, V. (ed.) CRYPTO 2005. LNCS, vol. 3621, pp. 378–394. Springer, Heidelberg (2005)
Evfimievski, A.V., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. In: Proc. of KDD 2002, pp. 217–228 (2002)
Frank, A., Asuncion, A.: UCI machine learning repository (2010)
Geisler, M.: Cryptographic Protocols: Theory and Implementation. PhD thesis, Aarhus University (2010)
Goethals, B.: Frequent set mining. In: The Data Mining and Knowledge Discovery Handbook, ch. 17, pp. 377–397. Springer (2005)
Goethals, B., Laur, S., Lipmaa, H., Mielikäinen, T.: On Private Scalar Product Computation for Privacy-Preserving Data Mining. In: Park, C., Chee, S. (eds.) ICISC 2004. LNCS, vol. 3506, pp. 104–120. Springer, Heidelberg (2005)
Kantarcioglu, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Transactions on Knowledge and Data Engineering 16(9), 1026–1037 (2004)
Mannila, H., Toivonen, H., Verkamo, A.I.: Efficient algorithms for discovering association rules. In: KDD Workshop, pp. 181–192 (1994)
Rizvi, S., Haritsa, J.R.: Maintaining data privacy in association rule mining. In: Proc. of VLDB 2002, pp. 682–693 (2002)
The Sharemind framework, http://sharemind.cyber.ee/
Shamir, A.: How to share a secret. Communications of the ACM 22(11), 612–613 (1979)
Toivonen, H.: Sampling large databases for association rules. In: Proc. of VLDB 1996, pp. 134–145. Morgan Kaufmann (1996)
Yang, Z., Wright, R.N., Subramaniam, H.: Experimental analysis of a privacy-preserving scalar product protocol. Computer Systems: Science & Engineering 21(1) (2006)
Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)
Zaki, M.J., Gouda, K.: Fast vertical mining using diffsets. In: Proc. of KDD 2003, pp. 326–335 (2003)
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Bogdanov, D., Jagomägis, R., Laur, S. (2012). A Universal Toolkit for Cryptographically Secure Privacy-Preserving Data Mining. In: Chau, M., Wang, G.A., Yue, W.T., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2012. Lecture Notes in Computer Science, vol 7299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30428-6_9
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DOI: https://doi.org/10.1007/978-3-642-30428-6_9
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