Machine Learning Differential Privacy With Multifunctional Aggregation in a Fog Computing Architecture

Data aggregation plays an important role in the Internet of Things, and its study and analysis has resulted in a range of innovative services and benefits for people. However, the privacy issues associated with raw sensory data raise significant concerns due to the sensitive nature of the user information it often contains. Thus, numerous schemes have been proposed over the last few decades to preserve the privacy of users’ data. Most methods are based on encryption technology, which is computationally and communicationally expensive. In addition, most methods can only handle a single aggregation function. Therefore, in this paper, we propose a multifunctional data aggregation method with differential privacy. The method is based on machine learning and can support a wide range of statistical aggregation functions, including additive and non-additive aggregation. It operates within a fog computing architecture, which extends cloud computing to the edge of the network, alleviating much of the computational burden on the cloud server. And, by only reporting the results of the aggregation to the server, communication efficiency is improved. Extensive experimental results show that the proposed method not only answers flexible aggregation queries that meet diversified aggregation goals, but also produces aggregation results with high accuracy.