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Distribution-Free Proofs of Proximity

Authors Hugo Aaronson , Tom Gur , Ninad Rajgopal , Ron D. Rothblum

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Author Details

Hugo Aaronson
  • Department of Computer Science and Technology, University of Cambridge, UK
Tom Gur
  • Department of Computer Science and Technology, University of Cambridge, UK
Ninad Rajgopal
  • Department of Computer Science and Technology, University of Cambridge, UK
Ron D. Rothblum
  • Faculty of Computer Science, Technion, Haifa, Israel

Funding

Tom Gur and Ninad Rajgopal are supported by the Tom Gur’s UKRI Future Leaders Fellowship MR/S031545/1. Tom Gur is also supported in part by EPSRC New Horizons Grant EP/X018180/1 and EPSRC RoaRQ Grant EP/W032635/1. Ron Rothblum is funded by the European Union (ERC, FASTPROOF, 101041208). Part of this work was completed when the first three authors were affiliated with the University of Warwick. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

Acknowledgements

We are grateful to Oded Goldreich for his insightful comments, some of which led to rephrasing Theorem 1 to indicate the trade-off between the query and communication complexities with better clarity. We are also thankful to Marcel Dall'Agnol for many helpful discussions.

Cite As Get BibTex

Hugo Aaronson, Tom Gur, Ninad Rajgopal, and Ron D. Rothblum. Distribution-Free Proofs of Proximity. In 39th Computational Complexity Conference (CCC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 300, pp. 24:1-24:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.CCC.2024.24

Abstract

Motivated by the fact that input distributions are often unknown in advance, distribution-free property testing considers a setting in which the algorithmic task is to accept functions f: [n] → {0,1} having a certain property Π and reject functions that are ε-far from Π, where the distance is measured according to an arbitrary and unknown input distribution 𝒟 ∼ [n]. As usual in property testing, the tester is required to do so while making only a sublinear number of input queries, but as the distribution is unknown, we also allow a sublinear number of samples from the distribution 𝒟. 
In this work we initiate the study of distribution-free interactive proofs of proximity (df-IPPs) in which the distribution-free testing algorithm is assisted by an all powerful but untrusted prover. Our main result is that for any problem Π ∈ NC, any proximity parameter ε > 0, and any (trade-off) parameter τ ≤ √n, we construct a df-IPP for Π with respect to ε, that has query and sample complexities τ+O(1/ε), and communication complexity Õ(n/τ + 1/ε). For τ as above and sufficiently large ε (namely, when ε > τ/n), this result matches the parameters of the best-known general purpose IPPs in the standard uniform setting. Moreover, for such τ, its parameters are optimal up to poly-logarithmic factors under reasonable cryptographic assumptions for the same regime of ε as the uniform setting, i.e., when ε ≥ 1/τ. 
For smaller values of ε (i.e., when ε < τ/n), our protocol has communication complexity Ω(1/ε), which is worse than the Õ(n/τ) communication complexity of the uniform IPPs (with the same query complexity). With the aim of improving on this gap, we further show that for IPPs over specialised, but large distribution families, such as sufficiently smooth distributions and product distributions, the communication complexity can be reduced to Õ(n/τ^{1-o(1)}). In addition, we show that for certain natural families of languages, such as symmetric and (relaxed) self-correctable languages, it is possible to further improve the efficiency of distribution-free IPPs.

Subject Classification

ACM Subject Classification
  • Theory of computation → Interactive proof systems
Keywords
  • Property Testing
  • Interactive Proofs
  • Distribution-Free Property Testing

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