ZK is a significant milestone for blockchain. As ZK technology approaches, privacy technology will once again gain attention! In that regard, it seems we should keep an eye on EVM L2 @tenprotocol, which merges ZK, privacy, and TEE. More and more data is being brought on-chain, and being able to use the Web3 ecosystem without exposing my identity or data is a very important value!
[A Small Study Log, Privacy] These days, there are keywords that frequently appear in the blockchain industry: ZK, TEE, MPC. All of them are technologies aimed at preserving privacy. So why are privacy technologies gaining attention again now? The reason is relatively clear. As the scope of data utilization rapidly expands, the structure that relied solely on public trust is hitting its limits. In particular, the advancement of AI has made this issue even more complex. This is not just a matter of personal data protection. In areas where the boundary between public and private is crucial, such as data exchange between companies, model training, finance, and healthcare, privacy issues are becoming even more pronounced. Looking at these technologies, I believe that blockchain is evolving from complete transparency to a structure where only necessary information can be verified, and many new and more innovative attempts are being made based on this. Each technology is implemented differently, but I think the goal is the same: to find a way to maintain trust without fully disclosing data. ___ 1️⃣ ZK - @cysic_xyz (Zero Knowledge Proof) Proves that a certain statement is true without disclosing data. It is used to verify the validity of transactions or to confirm that the results of an AI model meet specific conditions. 2️⃣ TEE - @tenprotocol (Trusted Execution Environment) Executes code in an isolated environment within the CPU. The process is not visible from the outside, but the results can be verified. 3️⃣ MPC - @BeldexCoin (Multi Party Computation) Multiple participants split the data to perform joint calculations. Each node cannot see the entire data, but reliable results are derived through collaborative computation.
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