In-Sync with the Scalability Narrative – zkSync Fundamental Analysis

Imagine a library with a magical bookkeeper. This bookkeeper has a unique talent: they can verify that a book contains specific knowledge without ever opening it. When someone needs to prove they’ve read a particular book, they don’t show the book’s content. Instead, they visit the bookkeeper, who somehow confirms their claim without peeking inside.

This magical process represents zero-knowledge (ZK) technology. The reader (prover) demonstrates their knowledge of the book (the secret) to the bookkeeper (verifier) without revealing the actual content. It’s as if the bookkeeper can sense the truth without seeing it, ensuring privacy and security. This way, secrets remain hidden while trust is maintained.

This is not the first euphemism CCI readers have encountered to illustrate the mechanics of ZK proofs. However, in order for zero-knowledge proofs to work, there are three characteristics that must be upheld:

• Completeness: Tests need to be able to verify that you know what you know within predefined conditions.

• Soundness: As a prover, it is impossible to cheat the verifier. 

• Zero-Knowledge: First condition is the prover is right, then the verifier knows it is right without knowing how the answer was reached, and with repetitions the same answer is generated. Second condition is there is no leakage of information that a third party could observe whether the process was scripted or genuine. 

The most digestible use case for ZK proofs is in blockchains and cryptocurrencies whereby we can know a wallet address and its contents, but we do not have an auditable path for how the transaction was approved. This use case is antithesis to blockchains primary value proposition of being open and auditable, but has value in instances where privacy is highly valued.

Another use case for ZK proofs is authentication for when passwords are not enough and higher security is required. Passwords can be compromised from a database or brute force hacked, with ZK authentication it is not possible to gain access with a password alone. 

An additional use case may be in the financial and insurance realms. Say you wanted to apply for a loan, you would usually need to disclose your bank account information that discloses how much money you have in your bank account. While most people do not want a third-party being able to scrutinize how much money you have and where you spend it, ZK proofs can verify that your bank account falls within an acceptable range in terms of assessing the ability to repay a loan. 

Given the instability in the macro, specifically toward perceived election integrity, ZK proofs offer confidence for voters. With ZK proofs, you can verify that you are eligible to vote and record your vote without revealing your identity. Finally, ZK proofs are utilized in machine learning whereby an algorithm can be verified as having correct outputs without compromising the process that an AI Language Learning Model undertook to reach that output.

As is tradition, we will seek to build upon previous editions of the CCI FA series that touched briefly on zkSync among several other potential airdrops (we will discuss the aftermath of this event) and have explored Loopring in a recent edition. It is likely accurate to say that zero-knowledge proofs are the most complex form of cryptography that has been implemented in Layer-2s (L2s) to date alongside sharding. In light of this, the current FA will revisit what ZK proofs are, what is under the hood of zkSync, discuss the challenges with implementing ZK proofs in the current investment climate and where that leaves zkSync.

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