Leveraging A Deep Learning-Based Privacy Compliance Framework For IoT Applications

This paper explores and investigates the development and implementation of a deep learning-based privacy compliance framework for Internet of Things (IoT) applications. The geometric growth in potentially sensitive data from the vast generations of data as IoT devices proliferate raises critical security and privacy concerns for individuals and organisations. Additionally, rising cybersecurity attacks reshape how data is handled, especially with IoT devices storing sensitive client data. Simultaneously, GDPR and CCPA which are rigorous data protection regulations have created an urgent and imperative need for automated compliance solutions. Utilising these regulatory standards, this research harnesses deep learning methodologies, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to tackle these complex challenges while employing public IoT datasets to test. Cutting-edge features are incorporated into the proposed deep-learning framework including threat characterising, anomaly detection, dynamic policy adjustment, and regulatory compliance checking/verification for IoT environments, with the additionality of a devised pipeline encompassing comprehensive data processing, model training, threat detection, compliance checking, and an intuitive user-friendly alert system. The findings concluded that the deep-learning models achieved high accuracy of 99% in threat detection, with general compliance achieving 91% and over of its IoT dataset (RT-IoT2022), concurrently while the integrated compliance checking functions effectively enforced privacy policies from a ‘docx’ file. This research contributes significantly to the relevant field by offering a novel, automated approach to handling privacy compliance in intricate IoT landscapes, prospectively reducing organisational resource expenditure while enhancing overall data protection measures.