Abstract

Functional encryption (FE) can provide a fine-grained access control on the encrypted message. Therefore, it has been applied widely in security business. The previous works about functional encryptions most focused on the deterministic functions. The randomized algorithm has wide application, such as securely encryption algorithms against chosen ciphertext attack, privacy-aware auditing. Based on this, FE for randomized functions was proposed. The existing constructions are provided in a weaker selective security model, where the adversary is forced to output the challenge message before the start of experiment. This security is not enough in some scenes. In this work, we present a novel construction for FE, which supports the randomized functionalities. We use the technology of key encapsulated mechanism to achieve adaptive security under the simulated environment, where the adversary is allowed to adaptively choose the challenge message at any point in time. Our construction is built based on indistinguishability obfuscation, non-interactive witness indistinguishable proofs and perfectly binding commitment scheme.

中文翻译：

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勘误：具有Q学习模型的多群布谷鸟搜索算法

摘要

功能加密（FE）可以对加密的消息提供细粒度的访问控制。因此，它已被广泛应用于安全业务中。以前有关功能加密的工作主要集中在确定性功能上。随机算法具有广泛的应用，例如针对所选密文攻击的安全加密算法，隐私感知审计。在此基础上，提出了用于随机函数的有限元。现有的结构是在较弱的选择性安全模型中提供的，在该模型中，对手被迫在实验开始之前输出质询消息。在某些场景中，这种安全性还不够。在这项工作中，我们提出了一种用于有限元的新颖结构，该结构支持随机功能。我们使用密钥封装机制技术在模拟环境下实现自适应安全性，在模拟环境下，允许对手在任何时间点自适应地选择质询消息。我们的构建基于不可区分的混淆，非交互的见证人不可区分的证据以及完美结合的承诺方案。