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Cited by 554

The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.

Random projection-based multiplicative data perturbation for privacy preserving distributed data mining

Kun Liu, Kargupta, H., Ryan, J.·2006·IEEE Transactions on Knowledge and Data Engineering
DOI 10.1109/tkde.2006.14
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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 privacy-preserving data mining applications.

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Metadata

Authors
Liu, K., Kargupta, H., & Ryan, J.
Venue
IEEE Transactions on Knowledge and Data Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2006-01
Volume
18
Issue
1
Pages
92-106
ISSN
1041-4347 (ISSN-L)
DOI
10.1109/tkde.2006.14
OpenAlex
W2160553465
Cited by
554
References
72
FWCI
35.59
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. https://app.cochat.ai/papers/10.1109/tkde.2006.14
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Liu, K., Kargupta, H., & Ryan, J. (2006). Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2006.14

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.