Getting it just right: towards balanced utility, privacy, and equity in shared space sensing

Abstract

Low-cost sensors have enabled a wide array of data-driven applications and insights. As a result, encountering spaces with pervasive sensing has become all but unavoidable. This creates a fundamental tension: the success of smart environments will become increasingly dependent on equity of access to data-driven insights and consideration of the privacy expectations of sensed individuals. These concerns highlight the need to bring equity to all stakeholders of smart environments, which in turn would preserve public trust in these smart spaces. In this work, we explored several approaches to identity-obscuring visual representations through a progressive series of experiments. We designed and validated a series of visual representations through stakeholder interactions and tested the ability of these visual representations to limit identification via a crowdsourced study. An evaluation across three months of data gathered within our organization also showed that the identity-obscured data could still be leveraged to accurately count group size. Our contributions lay the groundwork for sensing frameworks that bring utility to all stakeholders of shared spaces while being cognizant of their diverse privacy expectations.