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新论文 · v1arXiv:2611.11859

Doubly-Efficient Private Information Retrieval from Modular Forms

Alexandra Henzinger, Seyoon Ragavan

原文摘要

We present new information-theoretic protocols for $s$-server, doubly-efficient private information retrieval (PIR) in sublinear server time and one extra bit of space on the servers, for every constant $s \geq 2$. On an $n$-bit database, each of the $s$ PIR servers first builds and stores a preprocessing data structure consisting of $n+1$ bits (which is unique to each server, and after which the server may discard the original database). Then, each server answers a PIR query in time $T_s(n)$, by reading at most $T_s$ bits of its data structure and sending them to the user. In the two server setting ($s=2$), our PIR protocols achieve $T_2 = n^{0.82}$, matching the server time of the state-of-the-art schemes of Henzinger and Ragavan (EUROCRYPT 2026) while shrinking their storage from $O(n\sqrt{\log n})$ to $n+1$ bits. With $s \geq 3$ servers, our PIR protocols retain the $n+1$ bits of storage and additionally improve on the server time of all prior information-theoretic $s$-server schemes with $n^{1+o(1)}$ storage: for example, achieving $T_3 = n^{0.74}$ and $T_{10} = n^{0.61}$ (previously $n^{0.79}$ and $n^{0.75}$). Asymptotically, as the server count $s$ grows, we shrink the number of servers required to achieve a given server-work exponent from $s^{3.9}$ to $s$. Our results draw inspiration from matching-vector techniques (Efremenko, STOC 2009), which we generalize to handle degree-two computations in the exponent. Using this generalization, we build doubly-efficient PIR from the new intermediate abstraction of Fourier-sparse cone-delta functions. Then, we construct such Fourier-sparse cone-delta functions via a connection to modular forms and complex analysis: if a Fourier-sparse cone-delta function does not exist, then there exists a modular form which has too many roots, contradicting the valence formula.

cs.CR · math.NT

版本更新 · v3arXiv:2602.14320

Catalytic Tree Evaluation From Matching Vectors

Alexandra Henzinger, Edward Pyne, Seyoon Ragavan

原文摘要

We give new algorithms for tree evaluation (S. Cook et al. TOCT 2012) in the catalytic-computing model (Buhrman et al. STOC 2014). Two existing approaches aim to solve tree evaluation (TreeEval) in low space: on the one hand, J. Cook and Mertz (STOC 2024) give an algorithm for TreeEval running in super-logarithmic space $O(\log n\log\log n)$ and super-polynomial time $n^{O(\log\log n)}$. On the other hand, an earlier algorithm of J. Cook and Mertz (STOC 2020), run on a catalytic tape, solves TreeEval in logarithmic $O(\log n)$ free space and polynomial time, but with polynomial $O(\sqrt{n})$ catalytic space. We show that the latter result can be improved. We give a catalytic algorithm for TreeEval with logarithmic $O(\log n)$ free space, polynomial runtime, and subpolynomial $2^{O(\log^εn)}$ catalytic space (for any $ε> 0$). Our result also implies an improved simulation of time by catalytic space, via the reduction of Williams (STOC 2025). Our TreeEval algorithm comes from a framework, inspired by private information retrieval, that generically reduces tree evaluation to two combinatorial objects: matching-vector families and decoders (a new object we define that closely resembles decoding polynomials). These ingredients' parameters directly govern an algorithm's resources: the vectors' dimension determines its catalytic space, and the decoder's sparsity determines its free space and runtime. In addition to enabling our new algorithm, this framework explains existing tradeoffs, for example why the Cook-Mertz algorithm needs super-logarithmic space but no catalytic tape. It also lets better matching-vector families or decoders plug in directly to give better algorithms.

cs.DS