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Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006)
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| Venue: | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING |
| Citations: | 94 - 6 self |
BibTeX
@ARTICLE{Liu06randomprojection-based,
author = {Kun Liu and Hillol Kargupta and Jessica Ryan},
title = {Random projection-based multiplicative data perturbation for privacy preserving distributed data mining},
journal = {IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING},
year = {2006},
volume = {18},
number = {1},
pages = {92--106}
}
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Abstract
This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other data-mining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores Independent Component Analysis as a possible tool for breaching privacy in deterministic multiplicative perturbation-based models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projection-based technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacypreserving data mining applications.
Keyphrases
data mining privacy preserving random projection-based multiplicative data perturbation possible tool privacy protection independent component analysis privacy sensitive data many data-mining problem different ground approximate random projection-based technique certain statistical characteristic random rotation primary contribution multiplicative random projection matrix random orthogonal transformation principal component analysis multiple party inner product matrix deterministic multiplicative perturbation-based model correlation coefficient matrix data mining application different type euclidean distance matrix statistical aggregate experimental result extensive theoretical analysis
