Scalable Privacy-Preserving Data Mining with Asynchronously Partitioned Datasets

Hiroaki KIKUCHI; Daisuke KAGAWA; Anirban BASU; Kazuhiko ISHII; Masayuki TERADA; Sadayuki HONGO

doi:10.1587/transfun.E96.A.111

Scalable Privacy-Preserving Data Mining with Asynchronously Partitioned Datasets

Hiroaki KIKUCHI, Daisuke KAGAWA, Anirban BASU, Kazuhiko ISHII, Masayuki TERADA, Sadayuki HONGO

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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 synchronized 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 [2], 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^*.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E96-A No.1 pp.111-120

Publication Date: 2013/01/01

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Online ISSN: 1745-1337

DOI: 10.1587/transfun.E96.A.111

Type of Manuscript: Special Section PAPER (Special Section on Cryptography and Information Security)

Category: Public Key Based Protocols

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Hiroaki KIKUCHI, Daisuke KAGAWA, Anirban BASU, Kazuhiko ISHII, Masayuki TERADA, Sadayuki HONGO, "Scalable Privacy-Preserving Data Mining with Asynchronously Partitioned Datasets" in IEICE TRANSACTIONS on Fundamentals, vol. E96-A, no. 1, pp. 111-120, January 2013, doi: 10.1587/transfun.E96.A.111.
Abstract: 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 synchronized 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 [2], 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^*.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E96.A.111/_p

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@ARTICLE{e96-a_1_111,
author={Hiroaki KIKUCHI, Daisuke KAGAWA, Anirban BASU, Kazuhiko ISHII, Masayuki TERADA, Sadayuki HONGO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Scalable Privacy-Preserving Data Mining with Asynchronously Partitioned Datasets},
year={2013},
volume={E96-A},
number={1},
pages={111-120},
abstract={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 synchronized 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 [2], 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^*.},
keywords={},
doi={10.1587/transfun.E96.A.111},
ISSN={1745-1337},
month={January},}

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TY - JOUR
TI - Scalable Privacy-Preserving Data Mining with Asynchronously Partitioned Datasets
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 111
EP - 120
AU - Hiroaki KIKUCHI
AU - Daisuke KAGAWA
AU - Anirban BASU
AU - Kazuhiko ISHII
AU - Masayuki TERADA
AU - Sadayuki HONGO
PY - 2013
DO - 10.1587/transfun.E96.A.111
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E96-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2013
AB - 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 synchronized 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 [2], 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^*.
ER -