Privacy-Preserving Collaborative Filtering Schemes With Sampling Users

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概要

• 論文の詳細を見る

• A recommendation system enables us to take information from huge datasets about tastes effectively. Many cryptographical protocols for computing privacy-preserving recommendation without leaking the privacy of users are proposed. However, the current issue is the large computational overhead depending the number of users and hence, the application of the protocol is limited within small communities. In this paper, we propose some efficient schemes reducing the preference matrix of the sets of items and users. In the proposed schemes, users get their ratings encrypted by a public key of trusted authorities and submit to a public server so that the ciphertext of ratings are available to any users. The user wishing to have the recommendation performs some precomputations of these using the homomorphic property of public key algorithm and then sends the resulting ciphertext to the set of trusted servers. Having sampled some users rating in secure way, it divides the set of users into smaller groups which results in reduction of matrix in low dimension.

• Japan Society for Fuzzy Theory and Intelligent Informaticsの論文

著者

• KIKUCHI HIROAKI

  Tokai University

• KIZAWA Hiroatsu

  Tokai University, Graduate School of Engineering

• KIKUCHI Hiroaki

  Tokai University, School of Information and Telecommunication Engineering

関連論文

• Frequent Sequential Attack Patterns of Malware in Botnets
• Principal Component Analysis of Botnet Takeover
• Sender and Recipient Anonymous Communication without Public Key Cryptography
• Principal Component Analysis of Botnet Takeover
• Privacy-Preserving Collaborative Filtering Schemes With Sampling Users

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