Privacy-Preserving Reinforcement Learning Using Homomorphic Encryption in Cloud Computing Infrastructures
Privacy-Preserving Reinforcement Learning Using Homomorphic Encryption in Cloud Computing Infrastructures
复制标题
DOI：
10.1109/access.2020.3036899
复制
发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Jaehyoung Park;Dong Seong Kim;Hyuk Lim
中科院分区：
文献类型：
--
作者：
Jaehyoung Park;Dong Seong Kim;Hyuk Lim
强化学习（RL）是一种学习技术，它通过来自环境的反馈实现状态相关学习，并在没有环境先验知识的情况下做出最大化奖励的行动决策。如果这些RL技术用于在云计算上运行的以数据为中心的服务，则可能发生严重的数据隐私问题，因为需要在用户和云计算平台之间交换用于基于RL的服务的隐私相关的用户数据。我们考虑使用同态加密（HE）方案，这使得云计算平台能够在不解密密文的情况下执行算术运算。使用HE方案，允许用户仅向云计算平台递送密文以使用基于RL的服务。我们提出了一个隐私保护的强化学习（PPRL）框架的云计算平台。所提出的框架利用了一种基于错误学习（LWE）的密码系统进行全同态加密（FHE）。在多种基于云计算的智能服务场景下，对所提出的PPRL框架进行了性能分析和评估。
Reinforcement learning (RL) is a learning technique that enables state-dependent learning through feedback from an environment and makes an action decision for maximizing a reward without prior knowledge of the environment. If these RL techniques are used for data-centric services running on cloud computing, serious data privacy issues may occur because it is required to exchange privacy-related user data for RL-based services between the users and the cloud computing platform. We consider using homomorphic encryption (HE) scheme, which enables cloud computing platforms to perform arithmetic operations without decrypting ciphertexts. Using the HE scheme, users are allowed to deliver only ciphertexts to the cloud computing platform for using RL-based services. We propose a privacy-preserving reinforcement learning (PPRL) framework for the cloud computing platform. The proposed framework exploits a cryptosystem based on learning with errors (LWE) for fully homomorphic encryption (FHE). Performance analysis and evaluation for the proposed PPRL framework are conducted in a variety of cloud computing-based intelligent service scenarios.