Opportunistic mobile social networks: architecture, privacy, security issues and future directions

Mobile Social Networks and its related applications have made a very great impact in the society. Many new technologies related to mobile social networking are booming rapidly now-a-days and yet to boom. One such upcoming technology is Opportunistic Mobile Social Networking. This technology allows mobile users to communicate and exchange data with each other without the use of Internet. This paper is about Opportunistic Mobile Social Networks, its architecture, issues and some future research directions. The architecture and issues of Opportunistic Mobile Social Networks are compared with that of traditional Mobile Social Networks. The main contribution of this paper is regarding privacy and security issues in Opportunistic Mobile Social Networks. Finally, some future research directions in Opportunistic Mobile Social Networks have been elaborated regarding the data's privacy and security

Social Media, Ethics and the Privacy Paradox

Today’s information/digital age offers widespread use of social media. The use of social media is ubiquitous and cuts across all age groups, social classes and cultures. However, the increased use of these media is accompanied by privacy issues and ethical concerns. These privacy issues can have far-reaching professional, personal and security implications. Ultimate privacy in the social media domain is very difficult because these media are designed for sharing information. Participating in social media requires persons to ignore some personal, privacy constraints resulting in some vulnerability. The weak individual privacy safeguards in this space have resulted in unethical and undesirable behaviors resulting in privacy and security breaches, especially for the most vulnerable group of users. An exploratory study was conducted to examine social media usage and the implications for personal privacy. We investigated how some of the requirements for participating in social media and how unethical use of social media can impact users’ privacy. Results indicate that if users of these networks pay attention to privacy settings and the type of information shared and adhere to universal, fundamental, moral values such as mutual respect and kindness, many privacy and unethical issues can be avoided

PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA

Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary. First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbation methods add a noise signal, following a statistical distribution, to an original numerical matrix. With the help of analysis in eigenspace of perturbed data, the potential privacy vulnerability of a popular data perturbation is analyzed in the presence of very little information leakage in privacy-preserving databases. The vulnerability to very little data leakage is theoretically proved and experimentally illustrated. Second, in addition to numerical matrices, social networks have played a critical role in modern e-society. Security and privacy in social networks receive a lot of attention because of recent security scandals among some popular social network service providers. So, the need to protect confidential information from being disclosed motivates us to develop multiple privacy-preserving techniques for social networks. Affinities (or weights) attached to edges are private and can lead to personal security leakage. To protect privacy of social networks, several algorithms are proposed, including Gaussian perturbation, greedy algorithm, and probability random walking algorithm. They can quickly modify original data in a large-scale situation, to satisfy different privacy requirements. Third, the era of big data is approaching on the horizon in the industrial arena and academia, as the quantity of collected data is increasing in an exponential fashion. Three issues are studied in the age of big data with privacy preservation, obtaining a high confidence about accuracy of any specific differentially private queries, speedily and accurately updating a private summary of a binary stream with I/O-awareness, and launching a mutual private information retrieval for big data. All three issues are handled by two core backbones, differential privacy and the Chernoff Bound

Social Anchor: Privacy-Friendly Attribute Aggregation From Social Networks

In the last decade or so, we have experienced a tremendous proliferation and popularity of different Social Networks (SNs), resulting more and more user attributes being stored in such SNs. These attributes represent a valuable asset and many innovative online services are offered in exchange of such attributes. This particular phenomenon has allured these social networks to act as Identity Providers (IdPs). However, the current setting unnecessarily imposes a restriction: a user can only release attributes from one single IdP in a single session, thereby, limiting the user to aggregate attributes from multiple IdPs within the same session. In addition, our analysis suggests that the manner by which attributes are released from these SNs is extremely privacy-invasive and a user has very limited control to exercise her privacy during this process. In this article, we present Social Anchor, a system for attribute aggregation from social networks in a privacy-friendly fashion. Our proposed Social Anchor system effectively addresses both of these serious issues. Apart from the proposal, we have implemented Social Anchor following a set of security and privacy requirements. We have also examined the associated trust issues using a formal trust analysis model. Besides, we have presented a formal analysis of its protocols using a state-of-the-art formal analysis tool called AVISPA to ensure the security of Social Anchor. Finally, we have provided a performance analysis of Social Anchor

Credit Network Payment Systems: Security, Privacy and Decentralization

A credit network models transitive trust between users and enables transactions between arbitrary pairs of users. With their flexible design and robustness against intrusions, credit networks form the basis of Sybil-tolerant social networks, spam-resistant communication protocols, and payment settlement systems. For instance, the Ripple credit network is used today by various banks worldwide as their backbone for cross-currency transactions. Open credit networks, however, expose users’ credit links as well as the transaction volumes to the public. This raises a significant privacy concern, which has largely been ignored by the research on credit networks so far. In this state of affairs, this dissertation makes the following contributions. First, we perform a thorough study of the Ripple network that analyzes and characterizes its security and privacy issues. Second, we define a formal model for the security and privacy notions of interest in a credit network. This model lays the foundations for secure and privacy-preserving credit networks. Third, we build PathShuffle, the first protocol for atomic and anonymous transactions in credit networks that is fully compatible with the currently deployed Ripple and Stellar credit networks. Finally, we build SilentWhispers, the first provably secure and privacy-preserving transaction protocol for decentralized credit networks. SilentWhispers can be used to simulate Ripple transactions while preserving the expected security and privacy guarantees

Cybersecurity in social networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn recent years, the use of social networks has been increasing substantially. As we know, platforms such as Facebook, Twitter, Google +, Pinterest, LinkedIn or Instagram allow millions of individuals to create online profiles and share personal information with several friends through social networks – and, often, it’s possible to do the same with a large amount of strangers. By itself, social networks should not be considered a cyber threat. However, there are several issues related to maintaining the user’s data security and privacy, especially when they upload personal information, photos and / or videos. The large majority of users ignores the security best practices, which sometimes facilitates the hackers’ attacks. The main goal of this research is to understand patterns of information that are revealed on online social networks and their privacy implications. The goal is to map people behaviour on social networks and understand if they care about the security of their data exposed on the Internet. This research also aims to understand the impact of cybersecurity in social networks and a comparison of which social network is most concerned with the exposure of its user. It will be also addressed the current defence solutions that can protect social network users from these kinds of threats

Users’ Perception of Online Privacy and Security in Croatia – A Survey

This paper aims to obtain insight into users’ perception of security of their personal information on the Internet by conducting an opinion survey. A significant increase in Internet usage in the last 5 years in Croatia was not accompanied by the corresponding level of increase in digital competencies (Croatia is still below the European Union’s average). This research aims to answer the question of how much the average user in Croatia knows about the issues of user security and privacy on the Internet and mainly on social networks, with a somewhat greater focus on the younger population. The main contributions of this paper are: a) a survey on the security of personal data on the Internet (mainly social networks), b) processed data and data visualization, c) insight into what Internet users in Croatia know about Internet security and how they perceive their online privacy and d) insight into the willingness of users to further inform themselves on data protection

A Survey On Securing User Data in Social Networks Using Privacy Preserving Options

Privacy is one of greatest rubbing focuses that rises when interchanges get interceded in Online Social Networks (OSNs).different groups of software engineering analysts have been surrounded the 'OSN security issue' as one of the observation, institutional or social protection. On account of handling these issues they have likewise treated them as though they were free. The principle contends is that the diverse security issues are snared and that the examination on protection in OSNs would profit from a more all encompassing methodology. In this paper, we first give a prologue to the observation and social security viewpoint stressing the account that the educate them, and their suspicions, objectives and techniques. DOI: 10.17762/ijritcc2321-8169.15022