Enc2: Privacy-Preserving Inference for Tiny IoTs via Encoding and Encryption
Hao-Jen Chien, Hossein Khalili, Amin Hass, Nader Sehatbakhsh · 2023
Privacy-preserving machine learning (PPML) techniques have allowed remote and private inference for resource-constrained internet-of-things (IoT) devices on the cloud. The main challenge in most of the existing PPML technologies is a severe slowdown in inference latency mainly due to the use of encryption during the computation. To combat this, an emerging method is to leverage encoding as an alternative. While this results in a significant speedup, it imposes the burden of encoding to the resource-constrained IoT/edge device. Despite being feasible for simple workloads where encoding is lightweight, devices with very limited computational capabilities face a tradeoff between latency and privacy when performing complex tasks.