A Privacy Enforcing Framework for Data Streams on the Edge

Abstract

Recent developments in machine learning (ML) allow for efficient data stream processing and also help in meeting various privacy requirements. Traditionally, predefined privacy policies are enforced in resource-rich and homogeneous environments such as in the cloud to protect sensitive information from being exposed. However, large amounts of data streams generated from heterogeneous IoT devices often result in high computational costs, cause network latency, and increase the chance of data interruption as data travels away from the source. Therefore, this article proposes a novel privacy-enforcing framework for transforming data streams by executing various privacy policies close to the data source. To achieve our proposed framework, we enable domain experts to specify high-level privacy policies in a human-readable form. Then, the edge-based runtime system analyzes data streams (i.e., generated from nearby IoT devices), interprets privacy policies (i.e., deployed on edge devices), and transforms data streams if privacy violations occur. Our proposed runtime mechanism uses a Deep Neural Networks (DNN) technique to detect privacy violations within the streamed data. Furthermore, we discuss the framework, processes of the approach, and the experiments carried out on a real-world testbed to validate its feasibility and applicability.