Abstract
Due to the transparency of the wireless channel, users in multiple-key environment are vulnerable to eavesdropping during the process of uploading personal data and re-encryption keys. Besides, there is additional burden of key management arising from multiple keys of users. In addition, profile matching using inner product between vectors cannot effectively filter out users with ulterior motives. To tackle the above challenges, we first improve a homomorphic re-encryption system (HRES) to support a single homomorphic multiplication and arbitrarily many homomorphic additions. The public key negotiated by the clouds is used to encrypt the users’ data, thereby avoiding the issues of key leakage and key management, and the privacy of users’ data is also protected. Furthermore, our scheme utilizes the homomorphic multiplication property of the improved HRES algorithm to compute the cosine result between the normalized vectors as the standard for measuring the users’ proximity. Thus, we can effectively improve the social experience of users.
Highlights
With the rapid development of Internet technology, mobile devices such as mobile phones and tablets have gradually become popular in people’s daily life in recent years
U · v means the inner product of the two vectors u and v, and we use EPK(·) to denote the encryption function with the public key PK of the improved homomorphic re-encryption system (HRES) algorithm that will be introduced
In order to support homomorphic multiplication computations, a slight modification has been made on the original HRES algorithm
Summary
With the rapid development of Internet technology, mobile devices such as mobile phones and tablets have gradually become popular in people’s daily life in recent years. Gao et al.’s scheme utilizes an ElGamal-like proxy re-encryption [14] algorithm with additive homomorphic property, which leads to the issues of key management and the leakage of re-encryption keys. (ii) is paper utilizes the homomorphic multiplication property of the improved HRES algorithm to compute the cosine result between the normalized vectors as the standard for measuring proximity. The improved HRES algorithm can prove to be semantically secure, and the profile-matching protocol is secure in the sense that both clouds cannot get useful information about users’ data under the non-collusion security model. A friend finder can designate a target and initiate a matching query to the cloud; the two cloud servers return the matching result to the user through interactions In their scheme, the clouds perform most of the computations, which effectively reduces the burden of users.
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