We show that the space-bounded Statistical Zero Knowledge classes \({{{\sf SZK}_{\sf L}}} \) and \({{{\sf {NISZK}_{\sf {L}}}}} \) 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 [5], this can be viewed as lending support to the conjecture that these classes may coincide with the non-space-bounded classes \({{{\sf SZK}}} \) and \({{{\sf {NISZK}_{\sf {L}}}}} \) , respectively.

Eric Allender

Jacob Gray

Saachi Mutreja

Robustness for Space-Bounded Statistical Zero Knowledge

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
COMPUTER SCIENCE, THEORY & METHODS	3