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Cryptographic primitive of timed-release encryption (TRE) enables the sender to encrypt a message which only allows the designated receiver to decrypt after a designated time. Combined with other encryption technologies, TRE technology is applied to a variety of scenarios, including regularly posting on the social network and online sealed bidding. Nowadays, in order to control the decryption time while maintaining anonymity of user identities, most TRE solutions adopt a noninteractive time server mode to periodically broadcast time trapdoors, but because these time trapdoors are generated with fixed time server’s private key, many “ciphertexts” related to the time server’s private key that can be cryptanalyzed are generated, which poses a big challenge to the confidentiality of the time server’s private key. To work this out, we propose a concrete scheme and a generic scheme of security-enhanced TRE (SETRE) in the random oracle model. In our SETRE schemes, we use fixed and variable random numbers together as the time server’s private key to generate the time trapdoors. We formalize the definition of SETRE and give a provably secure concrete construction of SETRE. According to our experiment, the concrete scheme we proposed reduces the computational cost by about 10.8% compared to the most efficient solution in the random oracle model but only increases the almost negligible storage space. Meanwhile, it realizes one-time pad for the time trapdoor. To a large extent, this increases the security of the time server’s private key. Therefore, our work enhances the security and efficiency of the TRE.

In the past decades, the ever-increasing popularity of the Internet has led to an explosive growth of information, which has consequently led to the emergence of recommendation systems. A series of cloud-based encryption measures have been adopted in the current recommendation systems to protect users’ privacy. However, there are still many other privacy attacks on the local devices. Therefore, this paper studies the encryption interference of applying a differential privacy protection scheme on the data in the user’s local devices under the assumption of an untrusted server. A dynamic privacy budget allocation method is proposed based on a localized differential privacy protection scheme while taking the specific application scene of movie recommendation into consideration. What is more, an improved user-based collaborative filtering algorithm, which adopts a matrix-based similarity calculation method instead of the traditional vector-based method when computing the user similarity, is proposed. Finally, it was proved by experimental results that the differential privacy-based movie recommendation system (DP-MRE) proposed in this paper could not only protect the privacy of users but also ensure the accuracy of recommendations.

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