[Resource Topic] 2023/1672: Fine-grained Policy Constraints for Distributed Point Function

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Fine-grained Policy Constraints for Distributed Point Function

Authors: Keyu Ji, Bingsheng Zhang, Kui Ren


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.

ePrint: https://eprint.iacr.org/2023/1672

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