Crypto HODL / Life-long learner / Super Rick Fan / Life is a Meme.
Crypto HODL / Life-long learner / Super Rick Fan / Life is a Meme.
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As trend of privacy protection and trustless collabration is unstoppable in decentralized protocol, zk-proof is going to be widely implemented as technological infrastructure in blockchain network. Even though academic research in this space is booming, there is a huge gap for those working in crypto industry to both understand zk-proof theoretically and apply it appropriately. After researching into it diligently with abundant experience as a blockchain developer, I decided to write a series of articles of which theme is mainly tutoring beginners with underlying machanism and implemented examples of zk-proof.
Generally, ZK-Proof is a machanism or technique that can be used to prove the integrity and authenticity of secret knowledge without revealing it. The explanation for the concept is split into three parts for better understanding.
Sacrifice of privacy is necessary for winning over trust which is helpful in process of interaction with collaborators. The business logic of intermediary as custodial or censor authority comes from that situation. Naturally, blockchain protocols have non-custodial and anti-censor property which is realized with zk-proof. For example, a UTXO of bitcoin can be spent by zk-proof verification without custody of financial institution backed by giant capital or government.
The verification process is based on asymmetric cryptographic scheme(A detailed explanation of it will be delivered in later articles). Higher level of privacy including amount and receiver in a transaction is protected in ZCash protocol.
It’s proved solidly that privacy and reliability is compatible by more and more sophisticated applications of zk-proof.
Why it’s called zero knowledge not zero data or zero information even latters are more specific. Knowledge is kind of abstract and cannot be given a completely satisfactory definition. In information technology, knowledge is, to an enterprise or an individual, the possession of information or the ability to quickly locate it. There was an insightful interpretion from computer scientist Amit Sahai as below:
“The idea of zero-knowledge being something that you can already predict. If you can already predict the answer then you must not be gaining any knowledge by that interaction. This insight of being able to predict the future accurately, and that being an evidence of a lack of new knowledge”
In certain situation, if a vendor has invented a new pattern, it can be proved without revealing it with implementation of zero-knowledge until the protection period is over.
In some computational tolerable and interactive application, the probability of correctness of proof is 100% after repetitive calculation of part of secret information which is sampled from randomly selected secret instance. Taking map three color as a typical example, each time the prover showing the different colors of two neighboring countries can exponentially reduce the possibility of prover’s cheating manner.
In other succinct and non-interactive application, the proof offers a way to prove the correct execution of a defined computation, without disclosing the values used when performing that very computation.
Introduction of these three aspects of zk-proof is not profound or detailed enough for those who want to learn about it deeply and broadly, but it can be a good start for beginners to enjoy the journey of zk-proof.
As trend of privacy protection and trustless collabration is unstoppable in decentralized protocol, zk-proof is going to be widely implemented as technological infrastructure in blockchain network. Even though academic research in this space is booming, there is a huge gap for those working in crypto industry to both understand zk-proof theoretically and apply it appropriately. After researching into it diligently with abundant experience as a blockchain developer, I decided to write a series of articles of which theme is mainly tutoring beginners with underlying machanism and implemented examples of zk-proof.
Generally, ZK-Proof is a machanism or technique that can be used to prove the integrity and authenticity of secret knowledge without revealing it. The explanation for the concept is split into three parts for better understanding.
Sacrifice of privacy is necessary for winning over trust which is helpful in process of interaction with collaborators. The business logic of intermediary as custodial or censor authority comes from that situation. Naturally, blockchain protocols have non-custodial and anti-censor property which is realized with zk-proof. For example, a UTXO of bitcoin can be spent by zk-proof verification without custody of financial institution backed by giant capital or government.
The verification process is based on asymmetric cryptographic scheme(A detailed explanation of it will be delivered in later articles). Higher level of privacy including amount and receiver in a transaction is protected in ZCash protocol.
It’s proved solidly that privacy and reliability is compatible by more and more sophisticated applications of zk-proof.
Why it’s called zero knowledge not zero data or zero information even latters are more specific. Knowledge is kind of abstract and cannot be given a completely satisfactory definition. In information technology, knowledge is, to an enterprise or an individual, the possession of information or the ability to quickly locate it. There was an insightful interpretion from computer scientist Amit Sahai as below:
“The idea of zero-knowledge being something that you can already predict. If you can already predict the answer then you must not be gaining any knowledge by that interaction. This insight of being able to predict the future accurately, and that being an evidence of a lack of new knowledge”
In certain situation, if a vendor has invented a new pattern, it can be proved without revealing it with implementation of zero-knowledge until the protection period is over.
In some computational tolerable and interactive application, the probability of correctness of proof is 100% after repetitive calculation of part of secret information which is sampled from randomly selected secret instance. Taking map three color as a typical example, each time the prover showing the different colors of two neighboring countries can exponentially reduce the possibility of prover’s cheating manner.
In other succinct and non-interactive application, the proof offers a way to prove the correct execution of a defined computation, without disclosing the values used when performing that very computation.
Introduction of these three aspects of zk-proof is not profound or detailed enough for those who want to learn about it deeply and broadly, but it can be a good start for beginners to enjoy the journey of zk-proof.
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