Tulva: Practical Asynchronous Threshold FHE

AI Safety / AI Ethics FLEC
2026年10月14日
我们提出了 Tulva,一种实用的门限全同态加密(FHE)方案。该方案以模块化方式为任意基础 FHE 方案配备异步门限解密机制。本方案具有以下三方面优势:(1)几乎兼容所有基础 FHE 方案,且除在密文末尾附加一个随机标签外,完全不改变原有密文结构;(2)支持广泛的门限参数组合 $(t,K)$(即 $t$ 个参与者中任意 $K$ 个即可协同完成解密)以及不低于 256 的多项式环维度,并对每个明文比特仅需 6–25 比特的分片解密输出;(3)其安全性可基于标准的 LWE(学习容错)假设,在攻击者可向挑战密文发起分片解密查询的场景下仍得以保证。 我们提供了该方案的开源实现,对其门限解密性能进行了基准测试,并针对典型的隐私保护型以太坊虚拟机(EVM)应用场景评估了端到端的总体开销。在 16-out-of-512(即 512 个参与者中任意 16 个即可协作解密)的门限配置下,对一份已完成公开预处理的密文,执行依赖于私钥的分片解密计算与公开份额合并操作分别仅需 3.7 毫秒和 0.5 毫秒(运行于普通商用硬件)。相较于当前最先进的同步式门限 FHE 方案(Zyskind 等人,CCS’25),我们以一个公开、且依赖于具体密文的 0.5 秒预处理步骤,取代了其不依赖密文但需多方交互生成秘密相关随机数的准备阶段;同时,在线阶段彻底消除了交互需求,且在相同硬件上保持了相近的解密耗时,并将系统所能支持的总用户数提升了 32 倍。
We present Tulva, a practical threshold FHE scheme which equips a base FHE scheme with an asynchronous threshold decryption mechanism in a modular way. Our scheme simultaneously (1) is compatible with virtually any base FHE scheme and leaves its ciphertexts unchanged apart from an appended random tag, (2) supports a wide range of threshold profiles $(t,K)$ and ring degrees $\geq 256$ with 6-25 bits partial decryption per plaintext bit, and (3) achieves security in the presence of partial decryption queries on challenge ciphertexts based on the standard LWE assumption. We provide an open-source implementation, benchmark its threshold decryption performance and evaluate the end-to-end costs for typical privacy-preserving Ethereum Virtual Machine (EVM) use cases. For 16-out-of-512 threshold and a publicly preprocessed ciphertext, computing secret-dependent partial decryption and public share combination requires only 3.7 ms and 0.5 ms, respectively, on commodity hardware. Compared with the state-of-the-art synchronous scheme [Zyskind et al., CCS'25], we replace their ciphertext-independent yet interactive preparation of secret-correlated randomness with a public, ciphertext-dependent 0.5 s preprocessing step, eliminate interaction in the online phase while maintaining similar decryption time on the same machine, and support 32$\times$ more total users.
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