Ibrahim Yakut

Privacy-preserving hybrid collaborative filtering on cross distributed data

Data collected for collaborative filtering (CF) purposes might be cross distributed between two online vendors, even competing companies. Such corporations might want to integrate their data to provide more precise and reliable recommendations. However, due to privacy, legal, and financial concerns, they do not desire to disclose their private data to each other. If privacy-preserving measures are introduced, they might decide to generate predictions based on their distributed data collaboratively. In this study, we investigate how to offer hybrid CF-based referrals with decent accuracy on cross distributed data (CDD) between two e-commerce sites while maintaining their privacy. Our proposed schemes should prevent data holders from learning true ratings and rated items held by each other while still allowing them to provide accurate CF services efficiently. We perform real data-based experiments to evaluate our proposals in terms of accuracy. The results show that the proposed methods are able to provide precise predictions. Moreover, we analyze our schemes in terms of privacy and supplementary costs. We demonstrate that our schemes are secure, and online overhead costs due to privacy concerns are insignificant.

Efficient Integrity Verification for Outsourced Collaborative Filtering

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.

Efficient paillier cryptoprocessor for privacy-preserving data mining

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

Privacy-Preserving Two-Party Collaborative Filtering on Overlapped Ratings

To promote recommendation services through prediction quality, some privacy-preserving collaborative filtering solutions are proposed to make e-commerce parties collaborate on partitioned data. It is almost probable that two parties hold ratings for the same users and items simultaneously; however, existing two-party privacy-preserving collaborative filtering solutions do not cover such overlaps. Since rating values and rated items are confidential, overlapping ratings make privacy-preservation more challenging. This study examines how to estimate predictions privately based on partitioned data with overlapped entries between two e-commerce companies. We consider both user-based and item-based collaborative filtering approaches and propose novel privacy-preserving collaborative filtering schemes in this sense. We also evaluate our schemes using real movie dataset, and the empirical outcomes show that the parties can promote collaborative services using our schemes.

Privacy-Preserving Collaborative Filtering on Overlapped Ratings

To promote recommendation services through prediction quality, there are some privacy-preserving collaborative filtering (PPCF) solutions enabling e-commerce parties to collaborate on partitioned data. It is almost probable that both parties hold ratings for the identical users and items simultaneously; however existing PPCF schemes have not explored such overlaps. Since rating values and rated items are confidential, overlapping ratings makes privacy-preservation more challenging. This study examines how to estimate predictions privately based on partitioned data with overlapped entries between two e-commerce companies and we propose novel PPCF schemes in this sense.

Privacy-preserving item-based recommendations over partitioned data with overlaps

User ratings are vital elements to drive recommender systems and, in the case of an insufficient amount of ratings, companies may prefer to operate recommender services over partitioned data. To make this feasible, there are privacy-preserving schemes. However, such solutions currently have not comprehensively investigated probable rating overlaps among partitioned data. Such overlaps make collaboration over partitioned data more challenging, especially if overlapped values are divergent. In this study, we examine this privacy-preserving recommender problem and propose novel schemes in this sense. By means of our schemes, two parties can perform item-based collaborative filtering over partitioned data with divergent overlaps. We also show that the proposed solutions promote prediction quality with tolerable overheads.