Edited by ZKPunk
Highlights
𝒫𝔩𝔬𝔫𝒦: A Hands-On Deep Dive
zkSecurity offers a hands-on tutorial on 𝒫𝔩𝔬𝔫𝒦, helping readers understand its complex building blocks, including selector polynomials, wiring permutations, quotient tests, random challenges, and KZG commitments through step-by-step examples.
Circle STARKs: Part III, Circle FFT
This article delves into Circle FFT in Circle STARKs, explaining its principles and steps by comparing it with Cooley-Tukey FFT, and discusses the dimension gap in the polynomial space of Circle FFT and its impact.
Updates
The Science of Blockchain Conference 2025
Gödel's Incompleteness Theorem - Computerphile
How ZK inspired AI Watermarking with Miranda Christ
Papers
Design ZK-NR: A Post-Quantum Layered Protocol for Legally Explainable Zero-Knowledge Non-Repudiation Attestation
Coral: Fast Succinct Non-Interactive Zero-Knowledge CFG Proofs
BEAST-MEV: Batched Threshold Encryption with Silent Setup for MEV prevention
阈值加密内存池(Threshold encrypted mempools)能够在区块链交易被链上确认之前有效保护其隐私,是对抗去中心化区块链中抢跑攻击(MEV)的一种具有前景的方法。
近期的研究提出了两项加密方案在大规模去中心化区块链(如以太坊)中实现可扩展性所必须满足的关键性质:(1)静默设置(Silent Setup)[Garg-Kolonelos-Policharla-Wang, CRYPTO'24],要求阈值加密方案在初始化阶段不需要任何交互,仅依赖于公钥基础设施的存在;(2)批量解密(Batched Decryption)[Choudhuri-Garg-Piet-Policharla, USENIX'24],要求能够在不依赖于(或仅次线性依赖于)区块大小的通信开销下,对包含加密交易的整个区块进行解密,同时不泄露尚未被确认的交易隐私。
尽管现有构造分别实现了上述两项性质之一,但一个真正去中心化且具备可扩展性的加密内存池,必须同时满足这两项性质。在本文中,我们提出了首个基于双线性对构建的“支持静默设置的批量阈值加密方案”。我们对该原语进行了形式化定义,并在通用群模型(Generic Group Model)下给出了其安全性证明。此外,我们还提出了若干优化,并对所提出的方案进行了实现和性能评估。实验结果表明,该方案具有部署于区块链系统中的实际可行性和效率。
Data Availability Sampling with Repair
When Can We Incrementally Prove Computations of Arbitrary Depth?
qedb: Expressive and Modular Verifiable Databases (without SNARKs)
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