We initiate the study of zero-knowledge proofs for data streams. Streaming interactive proofs (SIPs) are well-studied protocols whereby a space-bounded algorithm with one-pass access to a massive stream of data communicates with a powerful but untrusted prover to verify a computation that requires large space. We define the notion of zero-knowledge in the streaming setting and construct zero-knowledge SIPs for the two main building blocks in the streaming interactive proofs literature: the sumcheck and polynomial evaluation protocols. To the best of our knowledge all known streaming interactive proofs are based on either of these tools, and indeed, this allows us to obtain zero-knowledge SIPs for central streaming problems such as index, frequency moments, and inner product. Our protocols are efficient in terms of time and space, as well as communication: the space complexity is $\mathrm{polylog}(n)$ and, after a non-interactive setup that uses a random string of near-linear length, the remaining parameters are $n^{o(1)}$. En route, we develop a toolkit for designing zero-knowledge data stream protocols, consisting of an algebraic streaming commitment protocol and a temporal commitment protocol. The analysis of our protocols relies on delicate algebraic and information-theoretic arguments and reductions from average-case communication complexity.
翻译:我们开始研究数据流的零知识证明。 流动互动证明( SIP) 是经过充分研究的规程, 即由空系算法, 以一通通取一流数据, 与强大但不信任的验证人进行交流, 以核实需要大空间的计算。 我们定义了在流流设置中的零知识概念, 为流动交互证明文献中的两个主要构件构建了零知识SIP: 总和和多元评价规程。 根据我们的知识, 所有已知的流动交互证都基于这些工具中的任何一个, 而且事实上, 这使得我们能够获得用于诸如指数、 频率时刻和内产等核心流问题的零知识SIP。 我们的规程在时间和空间以及通信方面是有效的: 空间复杂性是 $\ mathrm{polylog} (n), 在使用随机的直线长度设置后, 其余的参数是 $n ⁇ o(1)$。 在路径中, 我们开发了一条精细的数据协议的流流分析工具, 用于设计我们的平均数据协议削减。