Adaptive Security in SNARGs via iO and Lossy Functions

Brent Waters; Mark Zhandry

Paper 2024/254

Adaptive Security in SNARGs via iO and Lossy Functions

Brent Waters

, The University of Texas at Austin, NTT Research

Mark Zhandry

, NTT Research

Abstract

We construct an adaptively sound SNARGs in the plain model with CRS relying on the assumptions of (subexponential) indistinguishability obfuscation (iO), subexponential one-way functions and a notion of lossy functions we call length parameterized lossy functions. Length parameterized lossy functions take in separate security and input length parameters and have the property that the function image size in lossy mode depends only on the security parameter. We then show a novel way of constructing such functions from the Learning with Errors (LWE) assumption. Our work provides an alternative path towards achieving adaptively secure SNARGs from the recent work of Waters and Wu. Their work required the use of (essentially) perfectly re-randomizable one way functions (in addition to obfuscation). Such functions are only currently known to be realizable from assumptions such as discrete log or factoring that are known to not hold in a quantum setting.

Metadata

Available format(s): PDF
Category: Foundations
Publication info: Preprint.
Keywords: SNARGs indistinguishability obfuscation lossy functions LWE
Contact author(s): bwaters @ cs utexas edu
mzhandry @ gmail com
History: 2024-02-16: approved; 2024-02-16: received; See all versions
Short URL: https://ia.cr/2024/254
License: CC BY

BibTeX

@misc{cryptoeprint:2024/254,
      author = {Brent Waters and Mark Zhandry},
      title = {Adaptive Security in {SNARGs} via {iO} and Lossy Functions},
      howpublished = {Cryptology {ePrint} Archive, Paper 2024/254},
      year = {2024},
      url = {https://eprint.iacr.org/2024/254}
}