论文标题
在分布式数据上安全且有差异的私人贝叶斯学习
Secure and Differentially Private Bayesian Learning on Distributed Data
论文作者
论文摘要
数据集成和共享可以最大程度地增强新颖和有意义的发现的潜力。但是，这是一项非平凡的任务，因为从多个来源集成数据可以使研究参与者的敏感信息处于危险之中。为了解决隐私问题，我们通过预处理的随机梯度Langevin Dynamics与RMSPROP提出了分布式的贝叶斯学习方法，该方法在保护私人信息的同时以和谐的方式结合了差异隐私和同型加密。我们将提议的安全和隐私性的分布式贝叶斯学习方法应用于分布式数据上的逻辑回归和生存分析，并与集中式方法相比，在预测准确性和时间复杂性方面证明了其可行性。
Data integration and sharing maximally enhance the potential for novel and meaningful discoveries. However, it is a non-trivial task as integrating data from multiple sources can put sensitive information of study participants at risk. To address the privacy concern, we present a distributed Bayesian learning approach via Preconditioned Stochastic Gradient Langevin Dynamics with RMSprop, which combines differential privacy and homomorphic encryption in a harmonious manner while protecting private information. We applied the proposed secure and privacy-preserving distributed Bayesian learning approach to logistic regression and survival analysis on distributed data, and demonstrated its feasibility in terms of prediction accuracy and time complexity, compared to the centralized approach.
