Context-Aware Hybrid Encoding for Privacy-Preserving Computation in IoT Devices

Recent years have witnessed a surge in hybrid IoT-cloud applications where an end user distributes the desired computation between the IoT and cloud nodes. While achieving significant speed up, the major caveat of this approach is data privacy. Privacy-preserving methods have received major attention in the past few years, mainly because they can potentially solve this issue. Among several proposals, methods based on dynamic encoding and perturbation offer flexibility and low overhead. However, they often consider a weak adversary model or overlook practical limitations, such as encoding latency and complexity. This work proposes a new privacy-preserving method to address these issues. The key contributions of this article are twofold. First, unlike state-of-the-art, it proposes a new approach based on evolutionary algorithms to systematically evaluate the robustness of the encoding algorithm against a large population of potential adversaries. Second, it develops a dynamic obfuscation strategy that balances latency requirements in a realistic IoT-cloud hybrid ecosystem and privacy demands. Additionally, our method offers a unique benefit: it can be used alone for privacy protection, or it can be integrated with most existing methods to enhance privacy and reduce latency. The applicability and effectiveness of our proposed methods are thoroughly evaluated using two popular deep neural networks in a real-world IoT-cloud setting. We study the impact of our approach on important metrics, such as accuracy and privacy. Our results show that our proposed method can improve the overall privacy of a given IoT-cloud hybrid ecosystem by more than 10% on average.