LAUSR

Recent advances in sensor-enabled services have facilitated the use of mobile, wearable, and IoT devices; for example, an extensive range of sensor data are used to automatically track symptoms and diagnose health and well-being status of an individual (e.g., depression). As personal data are being continuously and unobtrusively sensed and collected at large scale, this raises privacy concerns in certain contexts (e.g., GPS data collection at privacy-sensitive places). Current one-off informed consent in such pervasive sensing scenarios does not offer context-awareness support that enables selective data disclosure based on a user’s needs or preferences (e.g., disabling GPS data collection when visiting hospitals). A lack of context-awareness support in informed consent would be a critical barrier to user acceptance of data-intensive pervasive computing for health and well-being. As an alternative method, we introduce the concept of “dynamic consent,” a type of informed consent that enables granular data consent and management, initially introduced in biomedical research for patient data management. We explore how this consent practice within biomedical research might inform usable privacy designs in pervasive computing by conducting a scoping review of dynamic consent literature and discussing future research directions.