Improved Trade-offs Between Amortization and Download Bandwidth for Linear HSS
<|reference_start|>Improved Trade-offs Between Amortization and Download Bandwidth for Linear HSS: A Homomorphic Secret Sharing (HSS) scheme is a secret-sharing scheme that shares a secret $x$ among $s$ servers, and additionally allows an output client to reconstruct some function $f(x)$ using information that can be locally computed by each server. A key parameter in HSS schemes is download rate, which quantifies how much information the output client needs to download from the servers. Often, download rate is improved by amortizing over $\ell$ instances of the problem, making $\ell$ also a key parameter of interest.   Recent work (Fosli, Ishai, Kolobov, and Wootters 2022) established a limit on the download rate of linear HSS schemes for computing low-degree polynomials and constructed schemes that achieve this optimal download rate; their schemes required amortization over $\ell = \Omega(s \log(s))$ instances of the problem. Subsequent work (Blackwell and Wootters, 2023) completely characterized linear HSS schemes that achieve optimal download rate in terms of a coding-theoretic notion termed optimal labelweight codes. A consequence of this characterization was that $\ell = \Omega(s \log(s))$ is in fact necessary to achieve optimal download rate.   In this paper, we characterize all linear HSS schemes, showing that schemes of any download rate are equivalent to a generalization of optimal labelweight codes. This equivalence is constructive and provides a way to obtain an explicit linear HSS scheme from any linear code. Using this characterization, we present explicit linear HSS schemes with slightly sub-optimal rate but with much improved amortization $\ell = O(s)$. Our constructions are based on algebraic geometry codes (specifically Hermitian codes and Goppa codes).<|reference_end|>
@article{blackwell2024improved,
    title={Improved Trade-offs Between Amortization and Download Bandwidth for
  Linear HSS},
    author={Keller Blackwell and Mary Wootters},
    journal={arXiv preprint arXiv:2403.08719},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08719},
    primaryClass={cs.IT math.IT}
}