论文标题
高度可扩展的安全汇总的秘密共享共享
Secret Sharing Sharing For Highly Scalable Secure Aggregation
论文作者
论文摘要
安全的多方计算（MPC）可以提高数据所有者的安全性和隐私性，同时允许分析师执行高质量的分析。安全汇总是一种安全的分布式机制，可以支持联邦深度学习，而无需受信任的第三方。在本文中，我们提出了具有次线性通信复杂性的高性能安全汇总协议。 我们的协议通过基于小组的方法实现了更大的沟通和计算效率。它类似于扩展到价值-aka梯度向量的秘密共享协议，但我们添加了额外的秘密共享股份股票本身秘密共享层。这样可以确保在标准的真实/理想安全范式中，在半honest和恶意设置中，服务器可能会与对手相交的私密性。 在具有5％腐败客户和5％辍学的恶意环境中，我们的协议可以在100,000,000成员和长度为100的联盟中汇总，同时要求每个客户仅与其他350个其他客户进行通信。该汇总的具体计算成本不到服务器的半秒不到半秒，客户端的计算成本少于100ms。
Secure Multiparty Computation (MPC) can improve the security and privacy of data owners while allowing analysts to perform high quality analytics. Secure aggregation is a secure distributed mechanism to support federated deep learning without the need for trusted third parties. In this paper we present a highly performant secure aggregation protocol with sub-linear communication complexity. Our protocol achieves greater communication and computation efficiencies through a group-based approach. It is similar to secret sharing protocols extended to vectors of values-aka gradients-but within groups we add an additional layer of secret sharing of shares themselves-aka sharding. This ensures privacy of secret inputs in the standard real/ideal security paradigm, in both semi-honest and malicious settings where the server may collude with the adversary. In the malicious setting with 5% corrupt clients and 5% dropouts, our protocol can aggregate over a federation with 100,000,000 members and vectors of length 100 while requiring each client to communicate with only 350 other clients. The concrete computation cost for this aggregation is less than half a second for the server and less than 100ms for the client.
