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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

- Publicized

- 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)

- Category

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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 -