PPDF: A Privacy-Preserving Cloud-Based Data Distribution System with filtering - ZHANG, Yudi (Institutional Knowledge at Singapore Management University, 2023)
Cloud computing has emerged as a popular choice for distributing data among both individuals and companies. Ciphertext-policy attribute-based encryption (CP-ABE) has been extensively used to provide data security and enable fine-grained access control. With this encryption technique, only users whose attributes satisfy the access policy can access the plaintext. In order to mitigate the computational overhead on users, particularly on lightweight devices, partial decryption has been introduced, where the cloud assists in performing the decryption computations without revealing sensitive information. However, in this process, the cloud obtains the user's attributes, thus infringing on the user's privacy. To address this issue, this article proposes a privacy-preserving cloud-based data distribution system with filtering (PPDF) to enable partial decryption without revealing the user's attributes. The proposed system also employs an edge server to assist the user in filtering out invalid ciphertexts, i.e., ciphertexts where the user's attributes do not satisfy the access policy, and transmit only the valid partially decrypted ciphertexts to the data receiver. Consequently, the proposed PPDF scheme achieves constant decryption cost for the data receiver. We provide a security proof and a performance evaluation of the proposed scheme, which confirms its effectiveness and practicality in various real-world applications.