Enhancing privacy and security within social networks

Recently, communication and sharing the information through online social network (OSN) grows very fast in day to day life. As user share important data through online social network, therefore providing security and privacy to the online social network becomes the challenging task for the researchers. Moreover, for increasing the social connections and get the information from the particular group of people user needs to search the new friends and add them in the friend list. Now a days there isa friend recommendation method which was used by many online social networks, this method recommended the friend list to the user. As the privacy of these friend recommendation methods is the most significance task for the researchers and for the online social networks. Basically an online social network focused on the millions people's profile to spend time on an online social network, also implementing an OSN clients social circles, by using recommended suggestions. In this system, the main focus is on privacy and security of the online social network. This system basically focuses to support the users of online social network by secure trust creation with a stranger. This is achieved with multi-hop recommendation process. In the proposed method, the system provides privacy to the online social network from which system is allowing users to enhance their social networks. Existing system used secure KNN algorithm for securing social network. By using KNN algorithm, distance based learning is unclear. It is sort of distance and their component to use for the best results and calculation is very expensive. To overcome this limitation and to improve the precision, the proposed frame-work makes use of secure social coordinate matching with a Naive Bayes classifier. Also the privcy of social coordinates is maintained with Pailier encryption algorithm. Experimental results demonstrate that the proposed system is secure and time efficient than the existing one. Memory consumption is also improved with the proposed system.

Online social networking usage is growing rapidly, especially among at-risk populations, such as men who have sex with men (MSM). However, little research has studied the relationship between online social networking usage and sexual risk behaviors among at-risk populations. One hundred and eighteen Facebook-registered MSM (60.1% Latino, 28% African American; 11.9% other) were recruited from online (social networking websites and banner advertisements) and offline (local clinics, restaurants and organizations) venues frequented by minority MSM. Inclusion criteria required participants to be men who were 18 years of age or older, had had sex with a man in the past 12 months, were living in Los Angeles, and had a Facebook account. Participants completed an online survey on their social media usage and sexual risk behaviors. Results from a multivariable regression suggest that number of sexual partners met from online social networking technologies is associated with increased: 1) likelihood of having exchanged sex for food, drugs, or a place to stay within the past 3 months; 2) number of new partners within the past 3 months; 3) number of male sex partners within the past 3 months; and 4) frequency of engaging in oral sex within the past 3 months, controlling for age, race, education, and total number of sexual partners. Understanding the relationship between social media sex-seeking and sexual risk behaviors among at-risk populations will help inform population-focused HIV prevention and treatment interventions.

The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network.

Modeling online social network users’ profile attribute disclosure behavior from a game theoretic perspective

This paper aims to identify user-centric features to calculate the strength of social ties between Online Social Network (OSN) users, and models the same using Latent Space Model (LSM). The modeling approach processes a socio-centric user-set as the users are directly (friend) or indirectly (friend-of-friend) related to a seed (target) user, which makes it easier to identify social ties between users as compared to random sampling from a set of diverse OSN users. For a given user, interaction data up to two levels is modeled and analyzed to generate a user-centric social network. Eleven different features related to Facebook have been identified to calculate the strength of social ties between users. LSM is used to visualize relationships in user-centric historical data and to estimate the probability of social ties between OSN users. The users are plotted using LSM in a three-dimensional (3D) social space around a seed user, and a link probability function is devised to calculate the probability of link between any two users with respect to the persona of the seed user. A sphere of influence around each user demarcating its active influence area is also identified and discussed in this paper.

Abstract—Nowadays, the majority of people utilise social networking sites on a daily basis. Numerous people create profiles on social networking websites every day and connect with others there, regardless of their location or time. False identities are used in additional malicious operations in addition to playing a significant part in advanced persistent threats. Users of social networking sites can benefit from them, but they also have to worry about the security of their personal information. We must first determine the user's social network accounts before we can determine who is endorsing threats in these platforms. Social media usage has dramatically increased in recent years, according to statistics. Social networking sites have made things easier as they allow us to connect to people effortlessly and converse with them without the requirement for physical meetups. One of the major issues with Online Social Networks (OSNs) is fake interaction, which is used to artificially boost an account's popularity. Because phoney involvement causes businesses to lose money, inaccurate audience targeting in advertising, inaccurate product prediction systems, and an undesirable social network atmosphere, its detection is essential. Based on the classification, it is required to distinguish between real and phoney profiles on social media.Several categorization techniques have traditionally been used to identify phoney social media accounts. However, there are ways to improve social media's ability to detect phoney profiles. The suggested effort uses technology and machine learning to boost the percentage of predicted phoney profiles. Chi-square technique is used in feature selection models to find the best data. The several machine learning methods, including the Logistic Regression and Random Forest algorithms, are used in the classification approach. The classification outcome based on recall, sensitivity, specificity, f1-score, accuracy, and precision. Keywords—Machine learning, online social networks, Instagram, Social media, Natural language process.

Online social networks were in the beginning used for private purposes, and over time were adopted by academic and business users. Thus, they have become an irreplaceable part of life in the modern 21st, 2.0 societies, particularly for the modern consumers. Their comprehensiveness has also been noticed by marketing experts who has led them to an extensive usage of online social networks as a very popular and widely used effective marketing tool. Following those trends, the main research question is: whether users of social networks look for information about the product with the intention of buying it or them some other motives. Every contact with the brand via social networks does not necessarily mean that the user is also an actual buyer or a potential buyer of the product or services. Therefore, traders, marketers, and community managers should focus on better understanding the motives of different groups of social networks users for standing along with their brands in virtual cosmos. In this paper, we present the results of research aimed at determining the primary motives driving the users of social networks to be connected with the brand. According to the research results, the respondents most frequently connected to the brand in order to have real time information about products and events related to the product. In addition, some study observations showed that the respondents were highly motivated to be connected with the brand to obtain certain benefits, while some respondents used social networks for self-presentation. This research has also confirmed that the respondents’ engagement in social networks positively affects self-esteem. However, some respondents are associated with brands because they want to participate in designing and improving products. The survey was undertaken in Croatia 2014 as a part of a research for a bachelor’s degree thesis.

With the rapid development of social networks, users pay more and more attention to the protection of personal information. However, the transmission of users’ personal information through social networks will inevitably lead to privacy leakage and make users attacked. A large amount of privacy information can be inferred from the content and social traces published by users, which leads to the rise of privacy inference technology for users in social networks. Social relationship inference and attribute inference are two basic attacks on users’ privacy in social networks. This is the first systematic review of privacy inference attacks in social networks. The purpose of this retrospective study is to provide a global summary, summarizing the time trend of the topic, and show the evolution of the topic in the past 15 years. In addition, this paper discusses the trend and development of this topic and finally looks forward to future work. The contribution of our work is helpful for researchers to continue their research in this field.

A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. Most of this work focuses on how to share social networks owned by organizations without revealing the identities or the sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their daily information-sharing activities. In this article, we approach the privacy issues raised in online social networks from the individual users’ viewpoint: we propose a framework to compute the privacy score of a user. This score indicates the user’s potential risk caused by his or her participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive information a user discloses, the higher his or her privacy risk. Also, the more visible the disclosed information becomes in the network, the higher the privacy risk. We develop mathematical models to estimate both sensitivity and visibility of the information. We apply our methods to synthetic and real-world data and demonstrate their efficacy and practical utility.

Users' addiction to online social networks is discovered to be highly correlated with their social connections in the networks. Dense social connections can effectively help online social networks retain their active users and improve the social network services. Therefore, it is of great importance to make a good prediction of the social links among users. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously. Formally, the social networks which share a number of common users are defined as the "aligned networks". With the information transferred from multiple aligned social networks, we can gain a more comprehensive knowledge about the social preferences of users in the pre-specified target network, which will benefit the social link prediction task greatly. However, when transferring the knowledge from other aligned source networks to the target network, there usually exists a shift in information distribution between different networks, namely domain difference. In this paper, we study the social link prediction problem of the target network, which is aligned with multiple social networks concurrently. To accommodate the domain difference issue, we project the features extracted for links from different aligned networks into a shared lower-dimensional feature space. Moreover, users in social networks usually tend to form communities and would only connect to a small number of users. Thus, the target network structure has both the low-rank and sparse properties. We propose a novel optimization framework, SLAMPRED, to combine both these two properties aforementioned of the target network and the information of multiple aligned networks with nice domain adaptations. Since the objective function is a linear combination of convex and concave functions involving nondifferentiable regularizers, we propose a novel optimization method to iteratively solve it. Extensive experiments have been done on real-world aligned social networks, and the experimental results demonstrate the effectiveness of the proposed model.

Online social networks (OSNs) have analyses large growth in recent years and become a saturation for hundreds of millions of Internet users. These OSNs offer to enforce attractive means for digital social interactions and information contribution, but also increase a number of security and privacy issues. Right to use manage mechanism is provide to restrict shared data, they currently do not provide any mechanism to minimize problem of multiuser shared data. To this end, we propose an approach to enable the protection of shared data associated with multiple users in OSNs. We gives a platform to user to share their data in secure manner. We also discuss a proof-of-concept prototype of approach as part of a framework on social network and provide usability study and system evaluation of our method.

This chapter shall analyze the options users of online social networks like Facebook have to adjust privacy settings. As the theoretical background of this evaluation, an institutional economics point of view shall be applied. Against this background, the following analysis of how Facebook as a provider of an online social network designs its platform in such a way that their own interests, as many users’ data to keep visible and searchable, are implemented. Both the GUI of the platform (website) and the various possibilities for mobile use, such as special “mobile” versions of the website and smartphone applications (apps) for various platforms, will be evaluated.

The daily exposure of social media users to propaganda and disinformation campaigns has reinvigorated the need to investigate the local and global patterns of diffusion of different (mis)information content on social media. Echo chambers and influencers are often deemed responsible of both the polarization of users in online social networks and the success of propaganda and disinformation campaigns. This article adopts a data-driven approach to investigate the structuration of communities and propaganda networks on Twitter in order to assess the correctness of these imputations. In particular, the work aims at characterizing networks of propaganda extracted from a Twitter dataset by combining the information gained by three different classification approaches, focused respectively on (i) using Tweets content to infer the “polarization” of users around a specific topic, (ii) identifying users having an active role in the diffusion of different propaganda and disinformation items, and (iii) analyzing social ties to identify topological clusters and users playing a “central” role in the network. The work identifies highly partisan community structures along political alignments; furthermore, centrality metrics proved to be very informative to detect the most active users in the network and to distinguish users playing different roles; finally, polarization and clustering structure of the retweet graphs provided useful insights about relevant properties of users exposure, interactions, and participation to different propaganda items.

Rumour Detection in Online Social Networks: Recent Trends

Sampling algorithms for stochastic graphs: A learning automata approach