Distilling at the Edge: A Local Differential Privacy Obfuscation Framework for IoT Data Analytics

Edge computing has emerged as a promising paradigm for delay-sensitive and context-aware IoT data analytics, through migrating data processing from the cloud to the edge of the network. However, traditional solutions adopting homomorphic encryption to achieve data protection and aggregation at edge servers are infeasible because of their heavy computational overhead. How to preserve data privacy while guaranteeing data utility in edge computing becomes an extremely important problem for IoT data analytics. In this article, we propose a local differential privacy obfuscation (LDPO) framework for IoT data analytics to aggregate and distill the IoT data at the edge without disclosing users' sensitive data. We first introduce the architecture and benefits of the LDPO framework, followed by some technical challenges in guaranteeing its performance. Then we present a preliminary implementation of the LDPO framework, and validate its performance in terms of privacy preservation level and data utility using real-world apps and datasets. Some future directions are finally envisioned for further research.