Paper 2024/937

Distributed Point Function with Constraints, Revisited

Abstract

Distributed Point Function (DPF) provides a way for a dealer to split a point function $f_{\alpha, \beta}$ into multiple succinctly described function-shares, where the function $f_{\alpha, \beta}$ for a special input $\alpha$, returns a special output value $\beta$, and returns a fixed value $0$ otherwise. As the security requirement, any strict subset of the function-shares reveals nothing about the function $f_{\alpha,\beta}$. However, each function-share can be individually evaluated on the common input $x$, and these evaluation results can then be merged together to reconstruct the value $f_{\alpha, \beta}(x)$. Recently, Servan-Schreiber et al. (S&P 2023) investigate the access control problem for DPF; namely, the DPF evaluators can ensure that the DPF dealer is authorized to share the given function with privacy assurance. In this work, we revisit this problem, introducing a new notion called DPF with constraints; meanwhile, we identify that there exists a subtle flaw in their privacy definition as well as a soundness issue in one of their proposed schemes due to the lack of validation of the special output value $\beta$. Next, we show how to reduce both the storage size of the constraint representation and the server's computational overhead from $O(N)$ to $O(\log N)$, where $N$ is the number of authorized function sets. In addition, we show how to achieve fine-grained private access control, that is, the wildcard-style constraint for the choice of the special output $\beta$. Our benchmarks show that the amortized running time of our logarithmic storage scheme is $2\times$ - $3\times$ faster than the state-of-the-art when $N=2^{15}$. Furthermore, we provide the first impossibility and feasibility results of the DPF with constraints where the evaluators do not need to communicate with each other.