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Posts: 5,361
Joined: Feb 2011
14-02-2011, 09:52 AM

presented by:
Waleed Wagealla, Sotirios Terzis and Colin English
The Smartlab research group
Department of Computer and Information Sciences

In context-aware systems, there is a high demand on providingprivacy solutions to users when they are interacting and exchanging personalinformation. Privacy in this context encompasses reasoning about trust andrisk involved in interactions between users. Trust, therefore, controls theamount of information that can be revealed, and risk analysis allows us toevaluate the expected benefit that would motivate users to participate in theseinteractions. In this paper, we propose a trust-based model for privacy controlin context-aware systems based on incorporating trust and risk. Through thisapproach, it is clear how to reason about trust and risk in designing andimplementing context-aware systems that provide mechanisms to protectusers’ privacy. Our approach also includes experiential learning mechanismsfrom past observations in reaching better decisions in future interactions. Theoutlined model in this paper serves as an attempt to solve the concerns ofprivacy control in context-aware systems. To validate this model, we arecurrently applying it on a context-aware system that tracks users’ location. Wehope to report on the performance evaluation and the experience ofimplementation in the near future.


Recent advances in networking, handheld computing and sensortechnologies have led to the emergence of context-aware systems. This newtechnology makes it possible to collect assorted contextual sensing information,such as computing context, user context, and physical context [1]. In this paper, anarrow definition of context information is used, referring only to locationinformation. In location-aware systems, sensors are usually allocated in variousplaces to facilitate the collection of users’ location information in as accurate amanner as possible. The sensed data is processed by the context information servers(CIS), and then disseminated to users on demand

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