@inproceedings{bde12dc79a4c4eabba303f98a79afdf5,
title = "Collaborative multiparty association rules mining with threshold homomorphic encryption",
abstract = "In this paper we introduce a new approach to multiparty association rules mining based on a polynomial representation of sets encrypted with a homomorphic threshold cryptosystem. We describe a homogeneous collaborative multiparty association rules mining protocol that is secure in a malicious model. Presented algorithm is designed to enhance security and privacy in distributed environments where a malicious adversary may deviate arbitrarily from the prescribed protocol as it attempts to compromise the privacy of the other parties{\textquoteright} inputs or the correctness of the obtained result. To the best of our knowledge, the protocol presented in this paper is the first multiparty association rules mining protocol that is secure against malicious adversaries in distributed systems.",
keywords = "Association rules mining, Data mining, Distributed data analytics, Malicious model, Privacy preservation, Threshold encryption",
author = "Marcin Gorawski and Zacheusz Siedlecki and Anna Gorawska",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015 ; Conference date: 18-11-2015 Through 20-11-2015",
year = "2015",
doi = "10.1007/978-3-319-27161-3\_22",
language = "English",
isbn = "9783319271606",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "251--263",
editor = "Perez, \{Gregorio Martinez\} and Albert Zomaya and Kenli Li and Guojun Wang",
booktitle = "Algorithms and Architectures for Parallel Processing - ICA3PP International Workshops and Symposiums, Proceedings",
address = "Germany",
}