PERTURBATION BASED RELIABILITY AND MAINTAINING AUTHENTICATION IN DATA MINING
Patil Dnyanesh, W Shaikh Zainoddin, Md. Nasim Akhtar, S Loknath · 2012
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. Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied perturbation-based PPDM approach introduces random perturbation to individual values to preserve privacy before data are published. In our setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine these diverse copies to jointly infer additional information about the original data that the data owner does not intend to release. Our solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on demand. This feature offers data owners maximum flexibility.