A Privacy Filter Framework for Internet of Robotic Things Applications

Abstract

Traditionally robots have been stand-alone systems. In recent years, however, they have increasingly been connected to external knowledge resources through the Internet of Things (IoT). These robots are thus becoming part of IoT and can realistically allocate Internet of Robotic Things (IoRT) technologies. IoRT can facilitate Human-Robot Interaction (HRI) at functional (commanding and programming) and social levels, as well as a means for remote-interaction. IoRT-HRI can cause privacy issues for humans, in part because robots can collect data using IoT and move in the real world, partly because robots can learn to read human social cues and adapt or correct their behavior accordingly. In this paper, we address the topic of privacy-preserving for IoRT- Hri applications. The objective is to design a data release framework called a Privacy Filter (PF) that can prevent an adversary from private mining information from the released data while keeping utility data. In the experiments, we test our framework on two accessible datasets: MNIST (hand-written digits) and UCI-HAR (activity recognition from motion). Our experimental results on these datasets show that PF is highly effective in removing private information from the dataset while allowing utility data to be mined effectively.