Implanting Life-Cycle Privacy Policies in a Context Database

Ambient intelligence (AmI) environments continuously monitor surrounding individuals' context (e.g., location, activity, etc.) to make existing applications smarter, i.e., make decision without requiring user interaction. Such AmI smartness ability is tightly coupled to quantity and quality of the available (past and present) context. However, context is often linked to an individual (e.g., location of a given person) and as such falls under privacy directives. The goal of this paper is to enable the difficult wedding of privacy (automatically fulfilling users' privacy whishes) and smartness in the AmI. interestingly, privacy requirements in the AmI are different from traditional environments, where systems usually manage durable data (e.g., medical or banking information), collected and updated trustfully either by the donor herself, her doctor, or an employee of her bank. Therefore, proper information disclosure to third parties constitutes a major privacy concern in the traditional studies

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN