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Title: Robustness for Space-Bounded Statistical Zero Knowledge
We show that the space-bounded Statistical Zero Knowledge classes SZK_L and NISZK_L are surprisingly robust, in that the power of the verifier and simulator can be strengthened or weakened without affecting the resulting class. Coupled with other recent characterizations of these classes [Eric Allender et al., 2023], this can be viewed as lending support to the conjecture that these classes may coincide with the non-space-bounded classes SZK and NISZK, respectively.  more » « less
Award ID(s):
2150186
PAR ID:
10492824
Author(s) / Creator(s):
; ; ; ;
Editor(s):
Megow, Nicole; Smith, Adam
Publisher / Repository:
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Date Published:
Journal Name:
Leibniz International Proceedings in Informatics (LIPIcs):Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)
Volume:
275
Page Range / eLocation ID:
56:1-56:21
Subject(s) / Keyword(s):
Interactive Proofs Theory of computation → Complexity classes
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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