The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
최근 개인정보 보호는 데이터 마이닝의 주요 이슈 중 하나가 되었습니다. 많은 데이터 마이닝 애플리케이션에서 데이터 세트의 값 빈도 또는 값 튜플을 계산하는 것은 반복적으로 사용되는 기본 작업입니다. 개인 정보 보호 데이터 마이닝의 맥락에서 여러 가지 개인 정보 보호 주파수 마이닝 솔루션이 제안되었습니다. 이러한 솔루션은 많은 개인 정보 보호 데이터 마이닝 작업에서 중요한 단계입니다. 각 솔루션은 특정 분산 데이터 시나리오에 대해 제공되었습니다. 본 문서에서는 소위 2부분 완전 분산 설정에서 개인정보 보호 주파수 마이닝을 고려합니다. 이 시나리오에서 데이터 세트는 두 명의 서로 다른 사용자가 각 레코드를 소유하는 다수의 사용자에게 배포됩니다. 한 사용자는 속성 하위 집합의 값만 알고 다른 사용자는 나머지 속성의 값을 알고 있습니다. 채굴자는 각 사용자의 개인정보를 보호하면서 값의 빈도 또는 값의 튜플을 계산하는 것을 목표로 합니다. 무작위화 기술을 기반으로 한 일부 솔루션은 이 문제를 해결할 수 있지만 개인정보 보호와 정확성 사이의 균형 문제로 어려움을 겪습니다. 우리는 정확성을 잃지 않고 각 사용자의 개인 정보를 보장하는 개인 정보 보호 주파수 마이닝을 위한 암호화 프로토콜을 개발합니다. 실험 결과는 우리의 프로토콜도 효율적이라는 것을 보여줍니다.
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부
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
부
@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},}
부
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 -