为去中心化学习设计匿名通信机制，保护用户身份隐私。
Source Anonymity for Private Random Walk Decentralized Learning
- 用公钥加密和随机路由隐藏消息来源身份
- 在随机正则图上实现理论保障的源匿名性
- 适合对数据隐私要求高的分布式学习场景
本文研究基于随机游走的去中心化学习，其中每轮迭代中，一个用户更新模型并发送给随机选择的邻居，直至满足收敛条件。数据隐私是去中心化学习中的核心挑战。我们提出一种基于公钥密码学与匿名化的隐私保护算法：用户更新模型后，使用目标用户的公钥加密，并通过网络传输至指定用户。关键思想是隐藏发送方身份，使接收方解密时无法识别源。难点在于设计依赖网络结构的概率分布，使接收方认为所有用户成为源的可能性相似。我们定义了该问题，并构建了一个在随机正则图上具备理论保障的匿名方案。
原文摘要 · Abstract (English)
This paper considers random walk-based decentralized learning, where at each iteration of the learning process, one user updates the model and sends it to a randomly chosen neighbor until a convergence criterion is met. Preserving data privacy is a central concern and open problem in decentralized learning. We propose a privacy-preserving algorithm based on public-key cryptography and anonymization. In this algorithm, the user updates the model and encrypts the result using a distant user's public key. The encrypted result is then transmitted through the network with the goal of reaching that specific user. The key idea is to hide the source's identity so that, when the destination user decrypts the result, it does not know who the source was. The challenge is to design a network-dependent probability distribution (at the source) over the potential destinations such that, from the receiver's perspective, all users have a similar likelihood of being the source. We introduce the problem and construct a scheme that provides anonymity with theoretical guarantees. We focus on random regular graphs to establish rigorous guarantees.
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