Adaptive Sharing for Online Social Networks: A Trade-off Between Privacy Risk and Social Benefit

Abstract

Abstract-Online social networks such as Facebook allow users to control which friend sees what information, but it can be a laborious process for users to specify every receiver for each piece of information they share. Therefore, users usually group their friends into social circles, and select the most appropriate social circle to share particular information with. However, social circles are not formed for setting privacy policies, and even the most appropriate social circle still cannot adapt to the changes of users' privacy requirements influenced by the changes in context. This problem drives the need for better privacy control which can adaptively filter the members in a selected social circle to satisfy users' requirements while maintaining users' social needs. To enable such adaptive sharing, this paper proposes a utility-based trade-off framework that models users' concerns (i.e. potential privacy risks) and incentives of sharing (i.e. potential social benefits), and quantifies users' requirements as a tradeoff between these two types of utilities. By balancing these two metrics, our framework suggests a subset of a selected circle that aims to maximise users' overall utility of sharing. Numerical simulation results compare the outcome of three sharing strategies in randomly changing contexts.

CrowdService

“…To protect privacy, a worker can control when to report availability to the platform or choose to send heartbeat messages without location information (which means only participating in location-independent tasks). Moreover, privacy policies, regulatory strategies, and computational algorithms (e.g., anonymity and obfuscation) [Krumm 2009;Yang et al 2014;Barhamgi et al 2016] could be used for protecting privacy data, which will be employed in the future work.…”

Live Blackboxes: Requirements for Tracking and Verifying Aircraft in Motion

“…Due to data sensitivity, we cannot release the entire datasets as is without permission. After careful anonymisation through adaptive sharing 16 and cloud-based privacy protection, 17 we will be able to provide the fully captured tracks of aircraft of the period of the past three weeks to peer researchers.…”

Learning to share: Engineering adaptive decision-support for online social networks

“…A considerable body of research has been devoted to address the information sharing problem raised by the increasing number of privacy incidents and regrets happening in OSNs [8], [26]- [34]. However, these approaches do not consider the situation when users may have made poor sharing decisions in the past.…”

“…However, these benefits come with an increased risk of privacy violation from oversharing or underusing privacy controls [4]- [8]. Many OSN platforms currently support privacy management through features such as static (user-defined) friendship groups as reusable shortcuts for sharing a single post with multiple contacts.…”