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Extracting Randomness from Samplable Distributions, Revisited

https://doi.org/10.1109/FOCS57990.2023.00092

Ball, Marshall; Goldin, Eli; Dachman-Soled, Dana; Mutreja, Saachi (November 2023, 2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS))

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Robustness for Space-Bounded Statistical Zero Knowledge

https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2023.56

Allender, Eric; Gray, Jacob; Mutreja, Saachi; Tirumala, Harsha; Wang, Pengxiang (January 2023, Leibniz International Proceedings in Informatics (LIPIcs):Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023))
Megow, Nicole; Smith, Adam (Ed.)
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.
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Improved semi-quantum key distribution with two almost-classical users

https://doi.org/10.1007/s11128-022-03663-x

Mutreja, Saachi; Krawec, Walter O. (September 2022, Quantum Information Processing)

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Robustness for Space-Bounded Statistical Zero Knowledge

Allender, Eric; Gray, Jacob; Mutreja, Saachi; Tirumala, Harsha; Wang, Pengxiang (October 2022, Electronic colloquium on computational complexity)

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Robustness for Space-Bounded Statistical Zero Knowledge, Eric Allender, Jacob Gray, Saachi Mutreja, Harsha Tirumala, and Pengxiang Wang, ECCC Report TR22-138 (2022).

Allender, Eric; Gray, Jacob; Mutreja, Saachi; Tirumala, Harsha; Wang, Pengxiang (October 2022, Electronic colloquium on computational complexity)

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