Improved Local Leakage Resilience of Shamir Secret Sharing and Worst-Case Optimal Polynomial Intersection
Abstract: We study two problems: Local Leakage Resilience (LLR) for Shamir secret sharing, and worst-case Optimal Polynomial Intersection (OPI). Both problems concern polynomials of degree less than , over a prime-order finite field . In LLR for Shamir secret sharing, one asks how much one can learn about given a few bits leaked from each of , for distinct non-zero evaluation points . In OPI, one is given input list , and wants to find a polynomial of degree less than so that for as many as possible. Leveraging recent connection between these two problems due to (Sun, Wootters 2026), we improve the state-of-the-art for both problems. For LLR, we show that there is some constant $δ> 0$ so that, as long as , Shamir secret-sharing is one-bit locally leakage resilient (meaning that one can learn only a negligible amount about ). This is the first result to break the so-called "one-half barrier" for LLR, and improves over the previous best known result, requiring (Kasser, 2025). For OPI, we give a quantum algorithm that finds a polynomial that agrees with at least a fraction of the lists in expectation, for every fixed $\varepsilon>0$, where is the \emph{semicircle law} of (Jordan et al., 2025). This improves previous algorithmic (and existential) results of (Jo, 2026) and (Horinaga, Yamakawa, 2026). We also give further improved existential results. We also adapt the hardness result of (Yamakawa, Zhandry, 2024) to apply to OPI (rather than a folded version); over large fields, this gives an unconditional separation between the quantum and classical hardness of OPI relative to a membership oracle.
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