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

Abstract

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.

A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things

“…It achieves an ACC of 94% and 93%, respectively. Finally, Zhou et al [ 81 ] proposed a Ciphertext Policy Attribute-Based Proxy Re-encryption (CP-ABPRE) scheme with accountability to protect user privacy in Edge Intelligence (EI) model sharing. The model is designed to enable flexible data access.…”

Introductory Chapter: Data Privacy Preservation on the Internet of Things

“…The scheme uses a convolutional neural network model evaluated with gender classification datasets Internet movie database (IMDB-Wiki) [36] and labeled faces in the wild (LFW) [37], achieving an accuracy of 94% and 93%, respectively. A data privacy-preserving scheme known named CP-ABPRE is presented by Zhou et al that works on a policy-based encryption approach [38]. The scheme is found to be robust against privacy attacks and has a low computational overhead required for its encryption and decryption processes.…”

How Can Applications of Blockchain and Artificial Intelligence Improve Performance of Internet of Things? -- A Survey

“…In order to ensure the flexibility of data access and data security in the IoT, the scheme proposed in [155] addresses the issues with the security and privacy in the edge intelligence (EI) model for the IoT, where ML models are trained in EI. The work [156] presents a survey of various…”

“…[152] Helps in storing and processing a large volume of wearable sensor data in IoT. [155] Addresses the issues with the security and privacy in the edge intelligence model for IoT. [156] Presents reinforcement and deep learning based solutions to combat different types of security attacks in IoT models.…”