Privacy Issue: From Static to Dynamic Online Social Networks

Today's societies have become more dependent on social networks in terms of communications and interactions. These networks contain most of the people's activities, which can be public or even personal events. In the last decade, social networks have turned into more prominent platforms in managing and organizing public events. The Egyptian revolution in 2011 and the Ukrainian revolution in 2014 are good reflections of such events. However, it is not known how much the privacy issue of users is revealed in the reality as a consequence of their online interactions. In this work, we investigate the privacy issue in online social networks and its reflection on real life. Our dataset was extracted from the Facebook groups/pages that were involved in the 2019 Iraqi October revolution. Our approach generates a static network using the collected dataset. Then, we investigate the generated static network in terms of detecting potential anomalies. After that, we project the static network (including its characteristics) into a dynamic environment and generate a dynamic network for investigating the privacy issue in the real life. The contribution of this work lies in projecting a real-world static network into a dynamic environment aiming at investigating users' privacy in the real world. Finally, this kind of approach has not been given enough attention in the literature and it is therefore deeply investigated in this article

The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences

Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.Comment: 17 pages, 4 figure

Twitter Sentiment Analysis: An Examination of Cybersecurity Attitudes and Behavior

This exploratory study examines the cybersecurity attitudes and actual behavior over time using the data collected on the social media microblogging platform, Twitter. We plan to use the sentiment analysis and text mining techniques on original tweets related to cybersecurity collected at two different time periods. Upon completion of this research, we would present the analysis of the relationship between the cybersecurity attitudes and behavior and how behaviors may be shaped by the attitudes. This research work aims to contribute to the extant literature in cybersecurity and endeavors to enhance our understanding of cybersecurity attitude and behavior by validating the proposed research model and hypotheses by using real-time, user-generated, social media data