论文标题
一种可扩展的保护隐私协作机器学习的方法
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
论文作者
论文摘要
我们考虑了一种协作学习方案，其中多个数据所有者希望共同培训逻辑回归模型，同时使他们的单个数据集与其他方保持私密。我们建议COPML，这是一个完全分区的培训框架，可以同时实现可扩展性和隐私保护。 COPML的关键思想是将单个数据集牢固地编码单个数据集，以在许多方面有效地分布计算负载，并在安全编码的数据上以分布式方式执行培训计算以及模型更新。我们提供COPML的隐私分析并证明其融合。此外，我们在实验上证明COPML可以在基准方案上实现训练的显着加速。我们的协议提供了针对具有无界计算能力的勾结各方（对手）的强大统计隐私保证，同时在培训时间内针对基准协议实现了高达$ 16 \ times $ speedup。
We consider a collaborative learning scenario in which multiple data-owners wish to jointly train a logistic regression model, while keeping their individual datasets private from the other parties. We propose COPML, a fully-decentralized training framework that achieves scalability and privacy-protection simultaneously. The key idea of COPML is to securely encode the individual datasets to distribute the computation load effectively across many parties and to perform the training computations as well as the model updates in a distributed manner on the securely encoded data. We provide the privacy analysis of COPML and prove its convergence. Furthermore, we experimentally demonstrate that COPML can achieve significant speedup in training over the benchmark protocols. Our protocol provides strong statistical privacy guarantees against colluding parties (adversaries) with unbounded computational power, while achieving up to $16\times$ speedup in the training time against the benchmark protocols.
