On Privacy Preserving Blockchains and zk-SNARKs

Shahla Atapoor
During last few years, along with blockchain technology, cryptocurrencies have found huge attention from both commercial and scientific perspectives. Cryptocurrencies are digital coins which use cryptographic tools to allow secure peer-to-peer monetary transactions. Bitcoin is the most well-known cryptocurrency that allows direct payments between pseudonyms without any third party. If a user's pseudonym is linked to her identity, all her transactions will be traceable, which will violate her privacy. To address this, various privacy-preserving cryptocurrencies have been proposed that use different cryptographic tools to achieve anonymous transactions. Zerocash is one of the most popular ones that uses zero-knowledge proofs to hide the source, destination and value of each transaction.
This thesis consists of two main parts. In the first part, after a short overview of some cryptocurrencies (precisely Bitcoin, Monero and Zerocoin), we will explain the construction of Zerocash cryptocurrency and discuss the intuition behind the construction. More precisely, we will introduce the deployed primitives and will discuss the role of each primitive in the construction of the coin. In particular, we explain zero-knowledge Succinct Non-Interactive Arguments of Knowledge (a.k.a. zk-SNARKs) that play the main role in achieving strong privacy in Zerocash.
Due to the importance of zk-SNARKs in privacy-preserving applications, in the second part of the thesis, we will present a new variation of Groth's 2016 zk-SNARK that currently is the most efficient pairing-based scheme. The main difference between the proposed variation and the original one is that unlike the original version, new variation guarantees non-malleability of generated proofs. Our analysis shows that the proposed changes have minimal effects on the efficiency of the original scheme and particularly it outperforms Groth and Maller's 2017 zk-SNARK that also guarantees non-malleability of proofs.
Graduation Thesis language
Graduation Thesis type
Master - Computer Science
Helger Lipmaa, Janno Siim, Karim Baghery
Defence year