Beyond Data Collection: Safeguarding User Privacy in Social Robotics

Abstract

In an era marked by the advanced capabilities of social robots in personal and public spaces, the issue of pervasive data collection by these entities becomes increasingly pertinent. Social robots, deployed by government entities, hospitals, and corporations, are at the forefront of gathering sensitive personal data, necessitating careful consideration of privacy concerns. The vast amounts of data collected by these robots, while beneficial for decision-making and fostering research, also pose significant privacy risks. In particular, the challenge intensifies when robots collect and potentially share data that includes sensitive personal information. This paper presents a user-friendly Differential Privacy (DP) library that addresses this challenge. The library incorporates a risk threshold and evaluates the potential impact of data disclosure to accurately quantify privacy levels. Designed for nontechnical users, it enables the secure release of statistical data without the risk of privacy breaches. With privacy breaches and re-identification becoming increasingly common, this library offers a robust solution for safeguarding individuals' privacy while facilitating the sharing of valuable insights.