Name
Abstract | ||
|---|---|---|
To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of individual data records have been proposed recently. In this paper, we present FRAPP, a generalized matrix-theoretic framework of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, FRAPP is used to demonstrate that (a) the prior techniques differ only in their choices for the perturbation matrix elements, and (b) a symmetric perturbation matrix with minimal condition number can be identified, maximizing the accuracy even under strict privacy guarantees. We also propose a novel perturbation mechanism wherein the matrix elements are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at only a marginal cost in accuracy. The quantitative utility of FRAPP, which applies to random-perturbation-based privacy-preserving mining in general, is evaluated specifically with regard to frequent-itemset mining on a variety of real datasets. Our experimental results indicate that, for a given privacy requirement, substantially lower errors are incurred, with respect to both itemset identity and itemset support, as compared to the prior techniques. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICDE.2005.8 | international conference on data engineering |
Keywords | DocType | Volume |
perturbation matrix elements,prior technique,data privacy,matrix-theoretic framework,client privacy,privacy-preserving mining framework,random variables,random perturbation,matrix algebra,privacy requirement,high-accuracy privacy-preserving mining,privacy-preserving mining,novel perturbation mechanism,data mining process,data mining,perturbation matrix element,frequent-itemset mining,real datasets,frapp framework,perturbation mechanism,symmetric perturbation matrix,electronic commerce,random variable,condition number,database systems,symmetric matrices | Conference | cs.DB/0407035 |
ISSN | ISBN | Citations |
1084-4627 | 0-7695-2285-8 | 82 |
PageRank | References | Authors |
3.60 | 25 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shipra Agrawal | 1 | 580 | 37.48 |
| Jayant R. Haritsa | 2 | 2004 | 228.38 |