Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying cDiversity Privacy preserving data publishing (PPDP) refers to the releasing of anonymized data for the purpose of research and analysis. A considerable amount of research work exists for the publication of data, having a single sensitive attribute. The practical scenarios in PPDP with multiple sensitive attributes (MSAs) have not yet attracted much attention of researchers. Although a recently proposed technique (p, k)Angelization provided a novel solution, in this regard, where onetoone correspondence between the buckets in the generalized table (GT) and the sensitive table (ST) has been used. However, we have investigated a possibility of privacy leakage through MSA correlation among linkable sensitive buckets and named it as xe2x80x9cfingerprint correlation                         attack.xe2x80x9d Mitigating that in this paper, we propose an improved solution xe2x80x9c                         anonymizationxe2x80x9d algorithm. The proposed solution thwarts the                   attack using some privacy measures and improves the onetoone correspondence to onetomany correspondence between the buckets in GT and ST which further reduces the privacy risk with increased utility in GT. We have formally modelled and analysed the attack and the proposed solution. Experiments on the realworld datasets prove the outperformance of the proposed solution as compared to its counterpart.

A Fast PrivacyPreserving MultiLayer Perceptron Using RingLWEBased Homomorphic Encryption Concerns about leaking privacy from data have been preventing from making good use of socalled big data, while privacypreserving data analysis would still be a promising research direction. In this paper, we propose PrivacyPreserving MultiLayer Perceptron (PPMLP) that can compute the prediction realtime using RingLWEbased homomorphic encryption. We implement the proposed PPMLP in the form of a twoparty model consisting of client and server. The former encrypts input data and receives a classification result from a server, and the latter performs prediction over encrypted data. This scheme enables a client to acquire prediction without revealing actual data contents against a server. The proposed PPMLP can make a fast prediction that requires up to 80 msec per input without a significant drop in classification accuracy compared to the convention multilayer perceptron for plaintexts.