JiangChSo's repositories
PFLM
Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradients. To achieve robustness to users dropping out, existing practical privacy-preserving federated learning schemes are based on (t, N)-threshold secret sharing. Such schemes rely on a strong assumption to guarantee security: the threshold t must be greater than half of the number of users. The assumption is so rigorous that in some scenarios the schemes may not be appropriate. Motivated by the issue, we first introduce membership proof for federated learning, which leverages cryptographic accumulators to generate membership proofs by accumulating users IDs. The proofs are issued in a public blockchain for users to verify. With membership proof, we propose a privacy-preserving federated learning scheme called PFLM. PFLM releases the assumption of threshold while maintaining the security guarantees. Additionally, we design a result verification algorithm based on a variant of ElGamal encryption to verify the correctness of aggregated results from the cloud server. The verification algorithm is integrated into PFLM as a part. Security analysis in a random oracle model shows that PFLM guarantees privacy against active adversaries. The implementation of PFLM and experiments demonstrate the performance of PFLM in terms of computation and communication.
Multi-party-Non-repudiation-Protocol-based-on-Blockchain-Technology
多方不可否认协议在电子商务和电子邮件等领域都有重要的应用。已知的协议大多依赖可信第三方TTP，但TTP的中心化特性和对可靠性的高要求造成了协议的通信瓶颈。本文利用区块链去中心化特性设计了一种分发不同消息的自适应多方不可否认协议。本文给出了协议的基本假设和符号说明，展示了协议的具体流程，并实现了协议的关键部分，描述了设计决策与实现步骤。同时本文使用形式化分析方法证明出该协议满足公平性、不可否认性和时限性等安全性质，将协议与已有的两类多方不可否认协议进行性能对比，分析表明新协议效率得到一定程度的提高。
2020-Huya-Program-Technical-Challenge
2020虎牙小程序技术挑战赛
AHEAD
The implementation of AHEAD