SACfe: Secure Access Control in Functional Encryption with Unbounded Data
Uddipana Dowerah, Subhranil Dutta, Frank Hartmann, Aikaterini Mitrokotsa, Sayantan Mukherjee, Tapas Pal · 2024
Privacy is a major concern in large-scale digital applications, such as cloud-computing, machine learning services, and access control. Users want to protect not only their plain data but also their associated attributes (e.g., age, location, etc). Functional encryption (FE) is a cryptographic tool that allows fine-grained access control over encrypted data. However, existing FE fall short as they are either inefficient and far from reality or they leak sensitive user-specific information. We propose SACfe, a novel attribute-based FE scheme that provides secure, fine-grained access control and hides both the user's attributes and the function applied to the data, while preserving the data's confidentiality. Moreover, it enables users to encrypt unbounded-length messages along with an arbitrary number of hidden attributes into ciphertexts. We design SACfe, a protocol for performing linear computation on encrypted data while enforcing access control based on inner product predicates. We show how SACfe can be used for online biometric authentication for privacy-preserving access control. As an additional contribution, we introduce an attribute-based linear FE for unbounded length of messages and functions where access control is realized by monotone span programs. We implement our protocols using the CiFEr cryptographic library and show its efficiency for practical settings.