A Privacy Frequent Itemsets Mining Framework for Collaboration in IoT Using Federated Learning

Abstract

Rapid advancement of industrial internet of things (IoT) technology has changed the supply chain network to an open system to meet the high demand for individualized products and provide better customer experiences. However the open-system supply chain has forced many small and midsize enterprises (SMEs) to adopt vertical integration by being divided into smaller companies with a distinctive business for each SME but a central alliance to produce a range of products and gain competencies. Therefore, existing models do not guarantee the protection of data privacy of individual SMEs. Moreover, especially for the IoT environment, collecting data in a secure way and revealing valuable knowledge in an IoT network is difficult. How to share data in a secure framework is of paramount importance in the internet of behavior field. In this article, a privacy-preserving data-mining framework is proposed for joint-venture industrial collaborative activities by combining federated learning and a “pre-large concept” of data-mining techniques. The novelty of the proposed approach is that, while mining high-utility itemsets (HUIs) from multiple datasets, it does not require direct data sharing. In the proposed method, the federated-learning framework can learn from aggregated learning parameters without scanning all data from different sets. The pre-large concept in this approach reduces the amount of scanning into different datasets. Thus, the approach makes it possible to train federated learning more quickly while protecting the privacy of individual data owners. The approach has been tested on real industrial datasets in a collaborative environment. Extensive experimental results show that the approach achieves high accuracy compared with conventional data-mining techniques while preserving the privacy of datasets.