Zhang et al. proposed FairZK in the paper, a scalable system to prove machine learning fairness in zero-knowledge, aiming to verify the fairness of machine learning models through ZK technology, providing scalable solutions for large-scale applications. Zhang等人在论文中提出了FairZK系统,旨在通过ZK技术验证机器学习模型的公平性,为大规模应用提供了可扩展的解决方案。
Jiang et al. proposed CoBBL in the paper, a dynamic constraint generation method for SNARKs, aiming to improve the efficiency and flexibility of proof generation. Jiang等人在论文中提出了CoBBL,一种为SNARKs动态生成约束的方法,旨在提高证明生成的效率和灵活性。