Shall I post this now? Optimized, delay-based privacy protection in social networks

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-016-1010-4Despite the several advantages commonly attributed to social networks such as easiness and immediacy to communicate with acquaintances and friends, significant privacy threats provoked by unexperienced or even irresponsible users recklessly publishing sensitive material are also noticeable. Yet, a different, but equally significant privacy risk might arise from social networks profiling the online activity of their users based on the timestamp of the interactions between the former and the latter. In order to thwart this last type of commonly neglected attacks, this paper proposes an optimized deferral mechanism for messages in online social networks. Such solution suggests intelligently delaying certain messages posted by end users in social networks in a way that the observed online activity profile generated by the attacker does not reveal any time-based sensitive information, while preserving the usability of the system. Experimental results as well as a proposed architecture implementing this approach demonstrate the suitability and feasibility of our mechanism.Peer ReviewedPostprint (author's final draft

Social Media and Fake News Detection using Adversarial Collaboration

The diffusion of fake information on social media networks obscures public perception of events, news, and relevant content. Intentional misleading news may promote negative online experiences and influence societal behavioral changes such as increased anxiety, loneliness, and inadequacy. Adversarial attacks target creating misinformation in online information systems. This behavior can be viewed as an instrument to manipulate the online social media networks for cultural, social, economic, and political gains. A method to test a deep learning model- long short-term memory (LSTM) using adversarial examples generated from a transformer model has been presented. The paper attempts to examine features in machine learning algorithms that propagate fake news. Another goal is to evaluate and compare the usefulness of generative adversarial networks with long-term short-term recurrent neural network algorithms in identifying fake news. A closer look at the mechanisms of implementing adversarial attacks in social media systems helps build robust intelligent systems that can withstand future vulnerabilities

Privacy preserving social network data publication

The introduction of online social networks (OSN) has transformed the way people connect and interact with each other as well as share information. OSN have led to a tremendous explosion of network-centric data that could be harvested for better understanding of interesting phenomena such as sociological and behavioural aspects of individuals or groups. As a result, online social network service operators are compelled to publish the social network data for use by third party consumers such as researchers and advertisers. As social network data publication is vulnerable to a wide variety of reidentification and disclosure attacks, developing privacy preserving mechanisms are an active research area. This paper presents a comprehensive survey of the recent developments in social networks data publishing privacy risks, attacks, and privacy-preserving techniques. We survey and present various types of privacy attacks and information exploited by adversaries to perpetrate privacy attacks on anonymized social network data. We present an in-depth survey of the state-of-the-art privacy preserving techniques for social network data publishing, metrics for quantifying the anonymity level provided, and information loss as well as challenges and new research directions. The survey helps readers understand the threats, various privacy preserving mechanisms, and their vulnerabilities to privacy breach attacks in social network data publishing as well as observe common themes and future directions

Mitigating Cybercrime and Online Social Networks Threats in Nigeria

Abstract: Many internet and Online Social Networks (OSN) users in Nigeria are victims of criminal activities perpetrated by those who take advantage of anonymity of their identities to harm users both in the virtual and in the real world on daily basis. Numerous security risks that exist in these networks include privacy violations, identity theft, and sexual harassment among others. A review of some of the different security and privacy risks which threaten the wellbeing of online social networks users including children is presented. This paper proposes a design that authenticate and uniquely identify every internet user most especially those on social networks. This paper further elicits inability to identify perpetrators as one of the reasons for the growing menace. A system that enables internet users' activities to be monitored in order to curb threats to online social networks in Nigeria is offered. It requires redefining the operations of Internet Service Providers (ISPs) that provide access for users on the internet and the National Communication Commission (NCC), a government body that oversees the information and communication system in Nigeria. A central database (cloud) is proposed to be hosted by the National Communication Commission with users' "activity log" feature. Users can send a request on any suspecting associate (pal) on social networks to get confirmation on genuine identity of the individual. Index Terms: Activity log, Anonymity, Cloud, Online Social Networks, Security and Privacy Risks, I INTRODUCTION n Nigeria, Social Networking sites such as facebook, Google+, Linkedln, Twitter et cetera, have gained a lot of popularity especially among the younger generations. Many of the users of social networks today are not aware of the threats associated with it. As a result of the larger user base and large amount of information available in social networks, it has become a potential channel for attackers and criminals to exploit.The threats include Identity Theft, Social Network Spam, Social. Network Malware, and Physical Threats. Social Networks can be described as web applications that allow users to create their semi-public profile. Semi-public profile is a profile that some information is public and some is private, it enables communication with those who are friends and thereby build an online community. Most importantly, it enables direct communication with these associates (pals) without restriction. Therefore large amount of their private information is shared in this social network space. The information shared ranges from bio-data information, contact information, comments, images, videos, et cetera Today, threats to Online Social Networks (OSN) are so pervasive that even academic community has not been able to provide or even suggest a holistic approach to curb the crimes often associated with internet with particular emphasis in this paper, on social networks. Efforts have been expended on identity protection that has not provided the needed results. In recent studies and from day-to-day experiences, many online social network users expose personal and intimate details about themselves, their friends, and their personality whether by posting photos or by directly providing information such as a home address and a phone number. As the use of online social networks becomes progressively more embedded into the everyday lives of users, personal information becomes easily exposed and abused. Information harvesting, by both the online social networks operators and by third-party commercial companies has recently been identified as a significant security concern for Online Social Networks (OSN) users. II THREATS OF SOCIAL NETWORKS The threats to social networks are becoming major setbacks for the technology of internet and its applications. The perpetrators use the online social networks (OSN) infrastructure to collect and expose personal information about users and their friends. They often lure users into clicking on specific malicious links. They include inference attacks, de-anonymization attacks, link reconstruction attacks, click jacking, Sybil attacks, socware, fake profile and identity clone attacks et cetera

A logic-based reasoner for discovering authentication vulnerabilities between interconnected accounts

With users being more reliant on online services for their daily activities, there is an increasing risk for them to be threatened by cyber-attacks harvesting their personal information or banking details. These attacks are often facilitated by the strong interconnectivity that exists between online accounts, in particular due to the presence of shared (e.g., replicated) pieces of user information across different accounts. In addition, a significant proportion of users employs pieces of information, e.g. used to recover access to an account, that are easily obtainable from their social networks accounts, and hence are vulnerable to correlation attacks, where a malicious attacker is either able to perform password reset attacks or take full control of user accounts. This paper proposes the use of verification techniques to analyse the possible vulnerabilities that arises from shared pieces of information among interconnected online accounts. Our primary contributions include a logic-based reasoner that is able to discover vulnerable online accounts, and a corresponding tool that provides modelling of user ac- counts, their interconnections, and vulnerabilities. Finally, the tool allows users to perform security checks of their online accounts and suggests possible countermeasures to reduce the risk of compromise