论文标题
技术巨头的MPC(GMPC):使Gulliver和Lilliputians能够合作合作
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably
论文作者
论文摘要
在这项工作中,我们介绍了Gulliver多方计算模型(GMPC)。 GMPC模型考虑了一个称为服务器或Gulliver的单个强大的聚会,该方案通过星形拓扑网络连接到$ n $用户(或者用作完整网络,服务器可以阻止任何消息)。用户的功能明显不如服务器,尤其是应该具有$ n $的Polyogarithmic的计算和通信复杂性。 GMPC模型中的协议应确保与可能破坏用户和/或服务器子集的恶意对手。 在GMPC模型中设计协议是一项精致的任务,因为用户只能保留有关其他用户(尤其是只能与其他用户的Polylog(n))通信的信息。此外,服务器可以阻止任何一对诚实派对之间的任何消息。因此,达成协议成为一项具有挑战性的任务。尽管如此,我们在GMPC模型中设计了通用协议,假设最多可能会损坏用户的$α<1/6 $(除服务器之外)。我们的主要贡献是Feige委员会选举方案[FOCS 1999]的变体,该方案在GMPC模型中是安全的。鉴于此工具我们显示: 1。假设完全同态加密(FHE),任何具有$ o \ left的计算有效函数(n \ cdot polylog(n)\ right)$ - 大小输出可以在GMPC模型中安全地计算出来。 2。任何可以通过$ o(polylog(n))$深度的电路计算的功能,$ o \ left(n \ cdot polylog(n)\ right)$ size,并且可以在GMPC模型中安全地计算出有限的fan-in和fan-Out。 3。特别是,可以在GMPC模型中安全地计算排序,而无需假设FHE。这对差异隐私的洗牌模型具有重要的应用,并解决了Bell等人的开放问题。 [CCS 2020]。
In this work, we introduce the Gulliver multi-party computation model (GMPC). The GMPC model considers a single highly powerful party, called the server or Gulliver, that is connected to $n$ users over a star topology network (alternatively formulated as a full network, where the server can block any message). The users are significantly less powerful than the server, and, in particular, should have both computation and communication complexities that are polylogarithmic in $n$. Protocols in the GMPC model should be secure against malicious adversaries that may corrupt a subset of the users and/or the server. Designing protocols in the GMPC model is a delicate task, since users can only hold information about polylog(n) other users (and, in particular, can only communicate with polylog(n) other users). In addition, the server can block any message between any pair of honest parties. Thus, reaching an agreement becomes a challenging task. Nevertheless, we design generic protocols in the GMPC model, assuming that at most $α<1/6$ fraction of the users may be corrupted (in addition to the server). Our main contribution is a variant of Feige's committee election protocol [FOCS 1999] that is secure in the GMPC model. Given this tool we show: 1. Assuming fully homomorphic encryption (FHE), any computationally efficient function with $O\left(n\cdot polylog(n)\right)$-size output can be securely computed in the GMPC model. 2. Any function that can be computed by a circuit of $O(polylog(n))$ depth, $O\left(n\cdot polylog(n)\right)$ size, and bounded fan-in and fan-out can be securely computed in the GMPC model without assuming FHE. 3. In particular, sorting can be securely computed in the GMPC model without assuming FHE. This has important applications for the shuffle model of differential privacy, and resolves an open question of Bell et al. [CCS 2020].