Learning from Context: A Field Study of Privacy Awareness System for Mobile Devices

Jedrzejczyk, L.; Mancini, C.; Corapi, D.; Price, B. A.; Bandara, A. K. and Nuseibeh, B. (2011). Learning from Context: A Field Study of Privacy Awareness System for Mobile Devices. Technical Report 2011/07; Department of Computing, The Open University.

DOI: https://doi.org/10.21954/ou.ro.000160b2


In this paper we investigate the effectiveness of context-awareness and machine learning in ensuring social acceptance of real-time feedback in a social location tracking system. Real-time feedback is a novel privacy feature supporting bi-directional function of privacy management. Its main function is to deliver feedback to the user by using an appropriate form of notification every time a user"s location has been checked. We evaluated our technology in the context of Buddy Tracker, our context-aware, location-sharing application for mobile devices. We report on our experience from the development process and also discuss findings of our field study with 15 participants. The findings show that context-awareness and machine learning can minimize the intrusiveness of real-time feedback, therefore making this important function socially more acceptable thus allowing users to benefit from the increased level of awareness that real-time feedback affords. We conclude with recommendations on how a better understanding of the user and application-specific context can improve the user experience and social acceptance of the system.

Viewing alternatives

Download history


Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions