On the Power of Statistical Zero Knowledge

Adam Bouland, Lijie Chen, D. Holden, J. Thaler, Prashant Nalini Vasudevan
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引用次数: 28

Abstract

We examine the power of statistical zero knowledge proofs (captured by the complexity class SZK) and their variants. First, we give the strongest known relativized evidence that SZK contains hard problems, by exhibiting an oracle relative to which SZK (indeed, even NISZK) is not contained in the class UPP, containing those problems solvable by randomized algorithms with unbounded error. This answers an open question of Watrous from 2002. Second, we lift this oracle separation to the setting of communication complexity, thereby answering a question of Göös et al. (ICALP 2016). Third, we give relativized evidence that perfect zero knowledge proofs (captured by the class PZK) are weaker than general zero knowledge proofs. Specifically, we exhibit oracles which separate SZK from PZK, NISZK from NIPZK and PZK from coPZK. The first of these results answers a question raised in 1991 by Aiello and Håstad (Information and Computation), and the second answers a question of Lovett and Zhang (2016). We also describe additional applications of these results outside of structural complexity.The technical core of our results is a stronger hardness amplification theorem for approximate degree, which roughly says that composing the gapped-majority function with any function of high approximate degree yields a function with high threshold degree.
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论统计零知识的力量
我们研究了统计零知识证明(由复杂性类SZK捕获)及其变体的力量。首先,我们给出了SZK包含困难问题的已知最强的相对证据,通过展示一个相对于SZK(实际上,甚至是NISZK)不包含在UPP类中的oracle,包含那些由具有无界误差的随机算法可解决的问题。这回答了2002年沃特劳斯提出的一个悬而未决的问题。其次,我们将这种oracle分离提升到通信复杂性的设置,从而回答了Göös等人的问题(ICALP 2016)。第三,我们给出了相对证据,证明完美零知识证明(由类PZK捕获)比一般零知识证明弱。具体来说,我们展示了区分SZK和PZK、NISZK和NIPZK、PZK和coPZK的预言机。这些结果中的第一个回答了Aiello和Håstad(信息与计算)在1991年提出的问题,第二个回答了Lovett和Zhang(2016)提出的问题。我们还描述了这些结果在结构复杂性之外的其他应用。我们研究结果的技术核心是一个更强的近似度的硬度放大定理,粗略地说,用任何高近似度的函数组成间隙多数函数,得到一个高阈值度的函数。
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