加密 101：完全同態加密 (#FHE) 發現完全同態加密 (FHE) 方案及其獨特的功能，以及最佳用例！深入挖掘 | 幣安廣場上的 Mind Network

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加密 101：完全同態加密 (#FHE)
發現完全同態加密 (FHE) 方案及其獨特的功能，以及最佳用例！
深入挖掘附圖中的細節！

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HashKey Capital Insights shared a comprehensive article explaining both the challenges and paradigm-shifting potential of Fully Homomorphic Encryption (#FHE). We were happy to contribute to this article alongside other prominent Web3 FHE projects. We're going to break it down for you and give you the takeaways. 1⃣ The big takeaway: FHE is the Holy Grail of Cryptography for a reason. It is changing the way we secure data across platforms, painting an almost unbelievable new era for data privacy. 2⃣ The primary limitations of FHE DevEx: 1) Easy front-end language 2) A fully functional FHE compiler 3) FHE schemes are too slow 3⃣ New solutions to these limitations: 1) Web3-specific FHE compilers offer the best performance without hardware accelerators 2) New FHE libraries that leverage the popular Web3 programming languages 3) Zama's toolkit exposes homomorphic operations as precompiled contracts 4⃣ ZKPs & FHE - a match made in privacy heaven. FHE allows anyone to perform computation on encrypted data. ZKPs allow one to prove something is true without revealing the underlying information itself. This is how they work together: 1) Making sure the ciphertext meets the requirements of the encryption scheme 2) Submitting a proof that an input plaintext satisfies a given application's conditions 3) The validator node needs to prove they've correctly executed the FHE computation 5⃣ ZKPs of ciphertext. ZKPs like SNARKS and STARKS do not rely on lattice cryptography; FHE does. This means that FHE is 'post-quantum'. In other words, ZKPs alone are not resistant to quantum computing attacks, but FHE is. 6⃣ The hardware problem. FHE on existing hardware is inefficient and not scalable in a decentralized way. Verifiable FHE solves this by allowing the computing party to submit a ZKP for proof of honest execution of transactions.

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Mind 101: Fully Homomorphic Encryption (#FHE) Fully Homomorphic Encryption (FHE) is a game-changer in data security, enabling computations on encrypted data without revealing its content. A critical tool for privacy in the digital age. At the core of #FHE is the ability to perform arithmetic operations on encrypted numbers and get an encrypted result, which, when decrypted, matches the result of operations as if they were done on plain data. The implications of #FHE are vast-secure voting systems, confidential medical records analysis, private financial transactions, and more. Anywhere data privacy is paramount, FHE can be the solution.

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Catch the speech presented by #MindNetwork's Head of Research at the EPF day! We discussed Fully Homomorphic Encryption (#FHE) based Stealth Address (A privacy-preserving and quantum computing resistant protocol).

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Big shoutout to our incredible 46K Minders for the support during the #ZeroTrustBridge campaign with BNB Chain! It has been a fruitful week for #MindNetwork. Check out the poster below for more details. The incentivized campaign on Galxe is still live！

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#Mind101: How does #ZeroTrustBridge (ZTB) address the compliance issue? The enhanced security and privacy of ZTB allows entities to achieve regulatory compliance. Here are two ways in which ZTB helps TradFi: 1. ZTB is used to encrypt and process KYC/AML data, ensuring constant protection even during processing. This meets regulations requiring banks to safeguard customer data. 2. ZTB is used to protect sender/receiver information so that information regarding the trade is not easily traceable but can still be verified by regulators/authorities. #MindNetwork

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