Scalable Privacy-Preserving Data Mining with Asynchronously Partitioned Datasets

Autor:	Daisuke Kagawa, Masayuki Terada, Anirban Basu, Hiroaki Kikuchi, Kazuhiko Ishii, Sadayuki Hongo
Rok vydání:	2011
Předmět:	Privacy preserving Naive Bayes classifier Intersection (set theory) Computer science Scalability Commutative encryption Data mining computer.software_genre Partition (database) computer Computer Science::Cryptography and Security
Zdroj:	IFIP Advances in Information and Communication Technology ISBN: 9783642214233 SEC
DOI:	10.1007/978-3-642-21424-0_18
Popis:	In the Naive Bayes classification problem using a vertically partitioned dataset, the conventional scheme to preserve privacy of each partition uses a secure scalar product and is based on the assumption that the data is synchronised amongst common unique identities. In this paper, we attempt to discard this assumption in order to develop a more efficient and secure scheme to perform classification with minimal disclosure of private data. Our proposed scheme is based on the work by Vaidya and Clifton[1], which uses commutative encryption to perform secure set intersection so that the parties with access to the individual partitions have no knowledge of the intersection. The evaluations presented in this paper are based on experimental results, which show that our proposed protocol scales well with large sparse datasets.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::07375e575d529febbd974e652d214734 https://doi.org/10.1007/978-3-642-21424-0_18 Zobrazit plný text záznamu