Abstract Paillier cryptosystem is extensively utilized as a homomorphic encryption scheme to ensure privacy requirements in many privacy‐preserving data mining schemes. However, overall performance of the applications employing Paillier cryptosystem intrinsically degrades because of modular multiplications and exponentiation operations performed by the cryptosystem. In this study, we investigate how to tackle with such performance degradation because of Paillier cryptosystem. We first exploit parallelism among the operations in the cryptosystem and interleaving among independent operations. Then, we develop hardware realization of our scheme using field‐programmable gate arrays. As a case study, we evaluate our cryptoprocessor for a well‐known privacy‐preserving set intersection protocol. We demonstrate how the proposed cryptoprocessor responds promising performance for hard real‐time privacy‐preserving data mining applications. Copyright © 2016 John Wiley & Sons, Ltd.

Collaborative filtering (CF) over large datasets requires significant computing power. Due to this data owning organizations often outsource the computation of CF (including some abstraction of the data itself) to a public cloud infrastructure. However, this leads to the question of how to verify the integrity of the outsourced computation. In this paper, we develop verification mechanisms for two popular item based collaborative filtering techniques. We further analyze the cheating behavior of the cloud from the game-theoretic perspective. Coupled with the right incentives, we can ensure that the computation is incentive compatible thus ensuring that a rational adversary will not cheat. Leveraging this, we can develop efficient and effective mechanisms to address the problem of integrity in outsourcing.

With increasing need for preserving confidential data while providing recommendations, privacy-preserving collaborative filtering has been receiving increasing attention. To make data owners feel more comfortable while providing predictions, various schemes have been proposed to estimate recommendations without deeply jeopardizing privacy. Such methods eliminate or reduce data owners' privacy, financial, and legal concerns by employing different privacy-preserving techniques. Although there are considerable numbers of studies focusing on privacy-preserving collaborative filtering schemes, there is no comprehensive survey investigating them with respect to different directions. In this survey, we mainly focus on studying various privacy-preserving recommendation methods according to the data partitioning cases and the utilized techniques for preserving confidentiality. We also review privacy in general and examine in collaborative filtering scenarios. We discuss the proposed schemes in terms of their limitations and practical implementation challenges. Moreover, we give an overview of evaluation of such schemes. We finally provide a comprehensive guideline for studying in this area and propose future research directions.