Privacy-Preserving Collaborative Filtering Schemes With Sampling Users

抄録

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.