Marlin: Preprocessing zkSNARKs with Universal and Updatable SRS

By Alessandro Chiesa, Yuncong Hu, Mary Maller, Pratyush Mishra, Psi Vesely, Nicholas P. Ward. IACR Cryptology ePrint Archive, 2020

499 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/6a346977fe0eb181c1de9ba636235727ea4ecc80

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Christian Rechberger
2. Arnab Roy
3. Dmitry Khovratovich
4. Alan Szepieniec
5. Lorenzo Grassi
6. Benedikt Bünz
7. Ben Fisch
8. Markus Schofnegger

In Computational Theory and Mathematics

1. Arnab Roy
2. Dmitry Khovratovich
3. Alan Szepieniec
4. Lorenzo Grassi
5. Benedikt Bünz
6. Christian Rechberger
7. Ben Fisch
8. Markus Schofnegger

In Computer Networks and Communications

1. Benedikt Bünz
2. Ben Fisch
3. Benjamin Wesolowski

Citations in the corpus

• Transparent SNARKs from DARK Compilers (2020, methodology): “We now evaluate Supersonic , the trustless-setup SNARK built on the Polynomial IOPs underlying Sonic [43], PLONK [30], and Marlin [22] and compiled using our DARK polynomial commitment scheme.”
• Poseidon: A New Hash Function for Zero-Knowledge Proof Systems (2021, methodology): “Since the results w.r.t. impossible differentials [BBS99], the multiple-of-8 property [GRR17], mixture differentials [Gra18] and integral distinguishers [DKR97] are equivalent to the ones proposed for H ADES MiMC, we omit their analysis here and we refer to [GLR + 20] for all details.”