A Survey on Privacy Issues in Mobile Social Networks

Abstract

Mobile Social Networks (MSNs) are a category of social networks that have features of both traditional OSNs and Location-Based Social Networks (LBSNs). MSNs provide users with an opportunity to explore social media, connect with people nearby, or with those sharing similar interests. The ease with which people can connect and use these networks to obtain information, share user-centric data, and request services on the go through their mobile devices as long as they have access to the internet has made MSNs omnipresent. Due to the widespread nature of MSNs and the lack of effective privacy-preserving architecture makes them a target for several attacks by adversaries and the users of the networks are completely vulnerable to these privacy breaches. With the advancements in technology, the number of attacks and their possible solutions is complex and expansive. Therefore, there is a need to properly categorize them to understand the intricacies of different attacks and the concepts used by researchers to devise different solutions. This survey on privacy issues in MSNs serves as a way to provide an introduction to MSNs. We categorize privacy in MSN on the basis of the aspect they effect and summarize the different threats in each category. Furthermore, we provide an elaborate classification of the various privacy-preserving solutions proposed in recent research works, under each category of privacy. We also review different datasets and data generation tools that can be used by future researchers in this field.

An Overview of using of Artificial Intelligence in Enhancing Security and Privacy in Mobile Social Networks

“…Identity theft occurs when an attacker impersonates a user to commit fraudulent activities, often damaging the user's reputation. Cyberstalking involves persistent online harassment, violating users' privacy and causing emotional distress [69]. On a larger scale, data breaches and the spread of misinformation pose serious threats.…”

Publishing Triangle Counting Histogram in Social Networks Based on Differential Privacy

“…Experiments use four real data sets from the Stanford Large Network Dataset Collection website [31]: (1) Facebook is an anonymous social data set;…”

“…In recent years, social networking sites such as Weibo, WeChat, Facebook, LinkedIn, and Twitter have changed the way people communicate online. People use social networking sites to make friends, seek jobs, share resources, share life [1], and so on. e massive data collected in social networks has become a rich source of insight into fundamental societal phenomena, such as epidemiology, information dissemination, marketing [2], and so on.…”

Efficient Privacy-Aware Forwarding for Enhanced Communication Privacy in Opportunistic Mobile Social Networks

“…However, this convenience comes at the cost of privacy. As messages are forwarded through intermediary nodes within these networks, personal data and user activities become increasingly vulnerable to exposure [5][6][7][8]. Therefore, OMSN privacy implications must be balanced with their benefits [7,9].…”