Vitalik “Reaching the Limits of Protocol Design” talk 101: He mentioned “BLS key aggregation is actually a technique that's at the heart of modern state consensus proof theory”, but what is the BLS key aggregation? A: The design core of aggregate signatures revolves around two concepts, "batching" and "compression": - “batching" refers to the correctness of signature results from multiple nodes being batch-verified, completing signature verification in one operation; - “compression" means that signatures from multiple nodes can be transformed into a unique digital signature, reducing storage consumption while establishing a data foundation for batch verification. BLS key aggregation brings us efficiency, scalability and security for aggregation / consensus. - Efficiency: This aggregation is crucial in state consensus protocols where numerous participants (nodes or validators) contribute their signatures to validate a particular state transition or operation. - Scalability: using BLS key aggregation significantly reduces the size of the proof or signature needed to validate the consensus. - Security: BLS key aggregation maintains the security properties of individual signatures while enabling their combination.

Curious about HTTPz in Web3? 🌐 Web2 relies on HTTP for data location via unique web addresses. In contrast, Web3, powered by blockchain, distributes data across networks and integrates AI into zero-trust HTTPS. Enter HTTPz by ZAMA: it adds end-to-end encryption to HTTPS, enhancing security. HTTPz is the epitome of future internet encryption. This evolution from HTTP to HTTPS to HTTPz underscores the criticality of privacy and security. 🔒#zama #security #Web3

AI x FHE In machine learning, there are two crucial processes: training and inference. AI now leverages Fully Homomorphic Encryption (FHE) for secure inference over encrypted data in a client/server deployment mode. Training happens over non-encrypted data, utilizing frameworks like concreteML. #MachineLearning #AI #FHE" Unveiling the power of machine learning models! 🚀 Three categories dominate the scene: - Tree-based models: decision trees, random forest, XGBoost - Linear Models: linear regression, logistic regression, SVM, elasticNet, Lasso, Ridge - Neural networks: Multi-layer perception, custom networks, torch, tensorflow. 🤖💡 #MLModels #AI #DataScience" The key question is how to convert a machine learning model into the FHE equivalent, and the key steps are as follows: - Convert every parameter to integer - Convert operations to integer operation (keep the mapping between integers and floating points) - Write the model inference using a FHE library - Chose the crypto parameters and expose key generation, quantization, encryption, decryption - Repeat the process for any change in the model More details please follow the next post!

Q: Can Zero-Knowledge Proof be supported in physical chips? 🧐 The answer is YES! Introducing the Accseal LEO Chip. Accseal Ltd. has just unveiled a groundbreaking achievement - the world’s first Zero-Knowledge Proof (ZKP) System-on-Chip (SoC) acceleration chip! Scheduled for mass production in Q1 2024, the Accseal LEO Chip utilizes cutting-edge 12nm processing, enabling intricate MSM and NTT operations. What's exciting is its flexibility - with programmable design, it can adapt to various ZK-SNARK algorithms like Aleo, Scroll, zkSync, Taiko, Aztec, Linea, and more. Plus, it's applicable to DePIN projects. Stay tuned for the future of secure data processing! 🔒 #ZeroKnowledgeProof #TechInnovation #AccsealLEOChip

Encryption 101! 🔐 A high level of Elliptic Curve Cryptography (ECC) explanation: A thread 🧵👇 #ECC #Crypto 1. Based on Complex Math: ECC uses the properties of elliptic curves in its calculations. These properties come from advanced mathematics. 2. Key Pairs for Security: ECC works by generating pairs of keys: one public and one private. The public key can be shared with anyone, while the private key is kept secret. These keys are linked mathematically, but it's extremely difficult to figure out the private key from the public key. 3. Encrypting and Decrypting Data: Data is encrypted (made unreadable) using the public key and can only be decrypted (made readable again) with the corresponding private key. This ensures that only the intended recipient, who possesses the correct private key, can access the information. 4. Used in Digital Signatures and Secure Communications: ECC is not just for encrypting data. It's also used for digital signatures, which are a way to verify the authenticity of digital messages or documents. This is crucial in many online transactions and communications.

1/ Encryption 101! 🔐 What is Elliptic Curve Cryptography (ECC)? A thread 🧵👇 #ECC #Crypto

Happy to help review this article from Hashkey. link.medium.com/jXPoQH2wkFb Recommend it to everyone who wants to go deeper with Fully homomorphic Encryption (#FHE) regardless of academic or industrial implementation!

2/3 Although TFHE-rs can support exportation of C/C++, C++ developers have to write their own version of TFHE library if they want to have more compatibility and better performance.

Encryption 101: Fully Homomorphic Encryption (#FHE) Discover Fully Homomorphic Encryption (FHE) schemes and their distinct features, along with the best use cases! Dig deeper into the details in the attached pic!

2/3 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.