Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting

The Dung LUONG; Tu Bao HO

doi:10.1587/transinf.E93.D.2702

Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting

The Dung LUONG, Tu Bao HO

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Recently, privacy preservation has become one of the key issues in data mining. In many data mining applications, computing frequencies of values or tuples of values in a data set is a fundamental operation repeatedly used. Within the context of privacy preserving data mining, several privacy preserving frequency mining solutions have been proposed. These solutions are crucial steps in many privacy preserving data mining tasks. Each solution was provided for a particular distributed data scenario. In this paper, we consider privacy preserving frequency mining in a so-called 2-part fully distributed setting. In this scenario, the dataset is distributed across a large number of users in which each record is owned by two different users, one user only knows the values for a subset of attributes, while the other knows the values for the remaining attributes. A miner aims to compute the frequencies of values or tuples of values while preserving each user's privacy. Some solutions based on randomization techniques can address this problem, but suffer from the tradeoff between privacy and accuracy. We develop a cryptographic protocol for privacy preserving frequency mining, which ensures each user's privacy without loss of accuracy. The experimental results show that our protocol is efficient as well.

Publication: IEICE TRANSACTIONS on Information Vol.E93-D No.10 pp.2702-2708

Publication Date: 2010/10/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E93.D.2702

Type of Manuscript: Special Section PAPER (Special Section on Data Mining and Statistical Science)

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The Dung LUONG, Tu Bao HO, "Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting" in IEICE TRANSACTIONS on Information, vol. E93-D, no. 10, pp. 2702-2708, October 2010, doi: 10.1587/transinf.E93.D.2702.
Abstract: Recently, privacy preservation has become one of the key issues in data mining. In many data mining applications, computing frequencies of values or tuples of values in a data set is a fundamental operation repeatedly used. Within the context of privacy preserving data mining, several privacy preserving frequency mining solutions have been proposed. These solutions are crucial steps in many privacy preserving data mining tasks. Each solution was provided for a particular distributed data scenario. In this paper, we consider privacy preserving frequency mining in a so-called 2-part fully distributed setting. In this scenario, the dataset is distributed across a large number of users in which each record is owned by two different users, one user only knows the values for a subset of attributes, while the other knows the values for the remaining attributes. A miner aims to compute the frequencies of values or tuples of values while preserving each user's privacy. Some solutions based on randomization techniques can address this problem, but suffer from the tradeoff between privacy and accuracy. We develop a cryptographic protocol for privacy preserving frequency mining, which ensures each user's privacy without loss of accuracy. The experimental results show that our protocol is efficient as well.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2702/_p

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@ARTICLE{e93-d_10_2702,
author={The Dung LUONG, Tu Bao HO, },
journal={IEICE TRANSACTIONS on Information},
title={Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting},
year={2010},
volume={E93-D},
number={10},
pages={2702-2708},
abstract={Recently, privacy preservation has become one of the key issues in data mining. In many data mining applications, computing frequencies of values or tuples of values in a data set is a fundamental operation repeatedly used. Within the context of privacy preserving data mining, several privacy preserving frequency mining solutions have been proposed. These solutions are crucial steps in many privacy preserving data mining tasks. Each solution was provided for a particular distributed data scenario. In this paper, we consider privacy preserving frequency mining in a so-called 2-part fully distributed setting. In this scenario, the dataset is distributed across a large number of users in which each record is owned by two different users, one user only knows the values for a subset of attributes, while the other knows the values for the remaining attributes. A miner aims to compute the frequencies of values or tuples of values while preserving each user's privacy. Some solutions based on randomization techniques can address this problem, but suffer from the tradeoff between privacy and accuracy. We develop a cryptographic protocol for privacy preserving frequency mining, which ensures each user's privacy without loss of accuracy. The experimental results show that our protocol is efficient as well.},
keywords={},
doi={10.1587/transinf.E93.D.2702},
ISSN={1745-1361},
month={October},}

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TY - JOUR
TI - Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting
T2 - IEICE TRANSACTIONS on Information
SP - 2702
EP - 2708
AU - The Dung LUONG
AU - Tu Bao HO
PY - 2010
DO - 10.1587/transinf.E93.D.2702
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2010
AB - Recently, privacy preservation has become one of the key issues in data mining. In many data mining applications, computing frequencies of values or tuples of values in a data set is a fundamental operation repeatedly used. Within the context of privacy preserving data mining, several privacy preserving frequency mining solutions have been proposed. These solutions are crucial steps in many privacy preserving data mining tasks. Each solution was provided for a particular distributed data scenario. In this paper, we consider privacy preserving frequency mining in a so-called 2-part fully distributed setting. In this scenario, the dataset is distributed across a large number of users in which each record is owned by two different users, one user only knows the values for a subset of attributes, while the other knows the values for the remaining attributes. A miner aims to compute the frequencies of values or tuples of values while preserving each user's privacy. Some solutions based on randomization techniques can address this problem, but suffer from the tradeoff between privacy and accuracy. We develop a cryptographic protocol for privacy preserving frequency mining, which ensures each user's privacy without loss of accuracy. The experimental results show that our protocol is efficient as well.
ER -