A Secure and Privacy-Preserving Machine Learning Model Sharing Scheme for Edge-Enabled IoT

With the popular use of IoT devices, edge computing has been widely applied in the Internet of things (IoT) and regarded as a promising solution for its wide distribution, decentralization, low latency. At the same time, in response to the massive computing data and intelligent requirements of various applications in the IoT, artificial intelligence (AI) technology has also achieved rapid development. As a result, edge intelligence (EI) for the Internet of Things has attracted widespread attention. Driven by the requirement that making full use of data, machine learning (ML) models trained in EI are usually shared. However, there may be some security and privacy issues due to the openness and heterogeneity of edge intelligence. How to ensure flexible data access and data security as well as the accountability for edge nodes and users in EI model sharing have become important issues. In this article, we propose a Ciphertext Policy Attribute Based Proxy Re-encryption (CP-ABPRE) scheme with accountability to address the security and privacy issues in EI model sharing. In our scheme, a user can delegate the access right to others to make model access more flexible. Furthermore, each entity that may need to be held accountable is embedded a unique ID to achieve traceability. Finally, security analysis and performance evaluation are given to prove that our scheme is CPA secure and does not lose much efficiency with more features.