Paper 2023/1672

Fine-grained Policy Constraints for Distributed Point Function

Abstract

Recently, Servan-Schreiber et al. (S&P 2023) proposed a new notion called private access control lists (PACL) for function secret sharing (FSS), where the FSS evaluators can ensure that the FSS dealer is authorized to share the given function with privacy assurance. In particular, for the secret sharing of a point function $f_{\alpha, \beta}$, namely distributed point function (DPF), the authors showed how to efficiently restrict the choice of $\alpha$ via a specific PACL scheme from verifiable DPF. In this work, we show their scheme is insecure due to the lack of assessment of $\beta$, and we fix it using an auxiliary output. We then propose more fine-grained policy constraints for DPF. Our schemes allow an attribute-based access control w.r.t. $\alpha$, and a template restriction for $\beta$. Furthermore, we show how to reduce the storage size of the constraint representation from $O(N)$ to $O(\log N)$, where $N$ is the number of constraints. Our benchmarks show that the amortized running time of our attribute-based scheme and logarithmic storage scheme is $2.5\times$ - $3\times$ faster than the state-of-the-art with $2^{15}$ constraints.