Multiparty unconditionally secure protocols
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Synthesizing High-Frequency Rules from Different Data Sources
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Peculiarity Oriented Multi-database Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An architecture for privacy-preserving mining of client information
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Privacy preserving mining of association rules
Information Systems - Knowledge discovery and data mining (KDD 2002)
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering Patterns in Flows: A Privacy Preserving Approach with the ACSM Prototype
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
BIDE-Based parallel mining of frequent closed sequences with mapreduce
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
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Research in the areas of privacy preserving techniques in databases and subsequently in privacy enhancement technologies have witnessed an explosive growth-spurt in recent years. This escalation has been fueled by the growing mistrust of individuals towards organizations collecting and disbursing their Personally Identifiable Information (PII). Digital repositories have become increasingly susceptible to intentional or unintentional abuse, resulting in organizations to be liable under the privacy legislations that are being adopted by governments the world over. These privacy concerns have necessitated new advancements in the field of distributed data mining wherein, collaborating parties may be legally bound not to reveal the private information of their customers. In this paper, we present a new algorithm PriPSeP (Privacy Preserving SEquential Patterns) for the mining of sequential patterns from distributed databases while preserving privacy. A salient feature of PriPSeP is that due to its flexibility it is more pertinent to mining operations for real world applications in terms of efficiency and functionality. Under some reasonable assumptions, we prove that our architecture and protocol employed by our algorithm for multi-party computation is secure.