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This paper surveys distributed point functions (DPFs) and their extension to function secret sharing (FSS), highlighting their lightweight structure and diverse applications in cryptography. The authors detail definitions, constructions, and the practical implications of DPFs for tasks such as private information retrieval and secure computation. Key findings indicate that efficient FSS schemes can generalize the applications of DPFs, thereby enhancing the utility of cryptographic primitives in various contexts.
Efficient function secret sharing could revolutionize how we approach secure computation and private data sharing across multiple parties.
A distributed point function (DPF) is a cryptographic primitive that enables compressed additive sharing of a secret weight-1 vector (equivalently, a point function) across two or more parties. The appealing lightweight structure of DPF constructions has enabled a wide range of applications. These include private information retrieval, anonymous messaging, secure computation with preprocessing, and pseudorandom correlation generators for expanding small correlated seeds into large pseudorandom instances of cryptographic correlations. In this article, we survey definitions, constructions, and applications of DPFs. We also discuss the extension of DPF to function secret sharing (FSS), which generalizes point functions to support richer function classes. Efficient FSS schemes yield a similar generalization for most of the applications of DPFs.