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.

中文翻译：

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移动社交网络中改进的云辅助隐私保护配置文件匹配方案

由于无线通道的透明性，多密钥环境中的用户在上载个人数据和重新加密密钥的过程中容易受到窃听。此外，由于用户的多个密钥而导致密钥管理的额外负担。此外，使用向量之间的内积进行配置文件匹配无法有效滤除具有别有用心的用户。为了解决上述挑战，我们首先改进了同态重加密系统（HRES），以支持单个同态乘法和任意多个同态加法。云协商的公钥用于对用户数据进行加密，从而避免了密钥泄露和密钥管理的问题，还保护了用户数据的隐私性。此外，我们的方案利用改进的HRES算法的同态乘法特性来计算归一化向量之间的余弦结果，以此作为测量用户接近度的标准。因此，我们可以有效地改善用户的社交体验。