A zero-one law for Boolean privacy
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Communications of the ACM
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Sharemind: A Framework for Fast Privacy-Preserving Computations
ESORICS '08 Proceedings of the 13th European Symposium on Research in Computer Security: Computer Security
FRAPP: a framework for high-accuracy privacy-preserving mining
Data Mining and Knowledge Discovery
SEPIA: privacy-preserving aggregation of multi-domain network events and statistics
USENIX Security'10 Proceedings of the 19th USENIX conference on Security
Constant-round multiparty computation using a black-box pseudorandom generator
CRYPTO'05 Proceedings of the 25th annual international conference on Advances in Cryptology
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
A practical implementation of secure auctions based on multiparty integer computation
FC'06 Proceedings of the 10th international conference on Financial Cryptography and Data Security
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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.