论文标题
实用安全分布深度学习的会员编写
Membership-Mappings for Practical Secure Distributed Deep Learning
论文作者
论文摘要
这项研究利用基于模糊的会员映射的数据表示能力，用于使用完全同构加密的实用安全分布式深度学习。通过应用模糊属性解决了由大型计算开销产生的完全同型加密数据的安全机器（深）学习的不切实际问题。基于全球收敛性和健壮的变分成员映射的局部深层模型引起了模糊属性。模糊属性以强大而灵活的方式结合了局部深层模型，以便可以使用由自举的二进制门组成的布尔电路以有效的方式对全局模型进行同派评估。所提出的方法在分布式学习方案中保留隐私，但仍保持准确，实用且可扩展。通过多种实验评估该方法，包括通过MNIST数据集和Freiburg杂货数据集进行演示。此外，考虑了与个人心理压力检测有关的生物医学应用。
This study leverages the data representation capability of fuzzy based membership-mappings for practical secure distributed deep learning using fully homomorphic encryption. The impracticality issue of secure machine (deep) learning with fully homomorphic encrypted data, arising from large computational overhead, is addressed via applying fuzzy attributes. Fuzzy attributes are induced by globally convergent and robust variational membership-mappings based local deep models. Fuzzy attributes combine the local deep models in a robust and flexible manner such that the global model can be evaluated homomorphically in an efficient manner using a boolean circuit composed of bootstrapped binary gates. The proposed method, while preserving privacy in a distributed learning scenario, remains accurate, practical, and scalable. The method is evaluated through numerous experiments including demonstrations through MNIST dataset and Freiburg Groceries Dataset. Further, a biomedical application related to mental stress detection on individuals is considered.
