Users In Mobile Social Networks Research Articles

Hyperconnectivity in action: users of mobile social networks and new technologies

PurposeThe aim of this research is to identify the characteristics that give rise to motivations to use social apps in light of behavioral concepts related to consumers’ desires and emotional values.Design/methodology/approachNetnography is used as the main methodology to analyze and categorize user profiles of online social networks. These profiles are presented through conceptual headlines, which highlight the main characteristics of each user group.FindingsThe results of the study show that many users have become dependent on the WhatsApp application, either for technological reasons or for social reasons related to fashion and status.Research limitations/implicationsFew consumers actually explained the ways they use mobile social networks in the context of the procedures and level of communication performed. However, the influence of social contexts in the consumer environment is changing perceived values focusing on prestige and status to technological elements that the majority of consumers use.Practical implicationsPractical implications are directly related to forming business connections in a less formal and more hedonic environment, improving market results while fostering user enjoyment. In addition, the ongoing updates to WhatsApp have brought new functionalities and improvements to previously weak features.Originality/valueAlthough other applications offer means by which to talk and send messages, WhatsApp continues to be (as of early 2021) the most used platform for conversation in Brazil. The sovereignty of WhatsApp is directly linked to its social value, which is related to the number of consumers who daily interact via the network.

Privacy protection scheme for mobile social network

In the recent years, popularity and number of users of mobile social networks have grown significantly. Due to different types of communication on these networks, users share a lot of information with each other. One of the most critical challenges of social networks is disclosure and unauthorized access to information, data, and communication between users that is a kind of violation of their privacy, which can be done by social network providers, especially unauthorized users. One way to protect one’s privacy is using encryption. Therefore, in the present paper, an improved secure design was presented for mobile social network using ciphertext-policy attribute-based encryption (CP-ABE) and advanced encryption standard (AES) that will encrypt users data in an end-to-end manner. In the proposed scheme by CP-ABE encryption, in addition to data privacy, the users’ desired access control is applied to the access of others to their shared data. As a result, unauthorized users will not be able to access and violate data privacy. Furthermore, it will prevent the unauthorized access of social network provider with the help of AES. This scheme provides new management to exchange encryption keys and manage network components for their information. Therefore, the keys are exchanged securely and some of them are generated outside reach of the social network server and by the user. The users' main keys are encrypted using the Rivest-Shamir-Adleman (RSA) method.Consequently, the proposed scheme will protect users privacy and prevent the unauthorized access to users’ data and information by the social network provider and unauthorized users by comparing results of the proposed scheme with other similar investigated schemes. In addition, it will be resistant to common attacks (man-in-the-middle attack and replay attack). Also, according to our findings, it is a safe and comprehensive scheme with high security and practicality while needing less time for both encrypting and decrypting the users messages.

The Effects of Internet Exposure on Sexual Risk Behavior Among Sexually Experienced Male College Students in China: Cross-sectional Study.

BackgroundAs a young subgroup, college students have become the main users of mobile social networks. Considering that people can indiscriminately access explicit sexual content on the internet, coupled with the increase of HIV infections in male college students, the role of the internet in meeting sexual partners and its correlation to risky sexual behavior has become an important topic.ObjectiveThe aim of this study is to explore the effects of internet exposure on sexual partners and sexual risk behavior among sexually experienced male college students.MethodsAn institution-based cross-sectional study design was used to collect data through a paper-based questionnaire administered to male college students recruited from colleges and gay organizations in Hangzhou, Zhejiang Province, China. A total of 1045 sexually experienced male students were incorporated in our analysis, with the following information collected: sociodemographic characteristics, sexual intercourse–related behaviors, and sexually transmitted disease (STD) knowledge. Mann-Whitney U and Kruskal-Wallis tests were used to examine differences regarding basic characteristics and sexual risk behaviors between male college students who meet sexual partners via the internet and those who do not. Sequential logistic regression models were employed to examine the influence of meeting sexual partners via the internet on risky sexual behaviors after controlling for other factors.ResultsThe mean age of the sexually experienced male students was 21.6 (SD 2.0) years. The likelihood of risky sexual behavior was varied, yet it was the highest for those who aim to meet paid sexual partners (145/192, 75.5% to 19/22, 86.4%), followed by those seeking partners for love or romance (258/435, 59.3%). Compared to non-internet partner seekers, internet partner seekers tended to have more casual intercourse (292/542, 53.9% versus 51/503, 10.1%), paid intercourse (32/542, 5.9% versus 12/503, 2.4%), and intercourse with same-sex partners (349/542, 64.4% versus 41/503, 8.2%); they were also more likely to use psychoactive drugs (125/349, 35.8% versus 5/41, 12.2%) and have more than 2 partners. With the increase of HIV and STD knowledge, the probability of having unprotected intercourse decreased for non-internet partner seekers. However, it increased for internet partner seekers with a rising HIV knowledge score. Sequential logistic regression showed that meeting sexual partners on the internet was statistically associated with sexual risk behaviors with multiple sexual partners (odds ratio 4.434; P<.001).ConclusionsMeeting sexual partners via the internet is a common behavior among sexually experienced male college students, and those who meet partners on the internet exhibited higher levels of risky sexual behaviors although they had sufficient HIV and STD knowledge; this is especially true for students who aimed to find partners for sexual intercourse. Thus, more attention should be paid to young adults to address the risky sexual behaviors that may contribute to STD spread among this population.

A User-Defined Location-Sharing Scheme with Efficiency and Privacy in Mobile Social Networks

The popularity of the modern smart devices and mobile social networks (MSNs) brings mobile users better experiences and services by taking advantage of location-aware capabilities. Location sharing, as an important function of MSNs, has attracted attention with growing popularity. While the users get great benefits and conveniences from MSNs, they also have high concerns about the privacy of location. However, in the existing solution, the privacy of users can hardly be guaranteed without the assumption of full trust in the service provider (SP), and few previous research studies have discussed the individual requirement of mobile users in MSNs. In this paper, we propose a user-defined location-sharing scheme (ULSS) to achieve enhanced privacy preservation under different contexts. We present a coarse-grained proximity detection method and a lightweight order-preserving encryption- (OPE-) based method to provide the users with flexible privacy preservation at different privacy levels. The proposed scheme preserves user’s location privacy with respect to SP, friends, and other adversaries, getting rid of the introduction of fully trusted party (TTP). Extensive experiments were conducted to verify the effectiveness and efficiency of our proposed scheme.

Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

Influence maximization based on activity degree in mobile social networks

SummaryThe problem of influence maximization (IM) has become an important research topic due to the rapid growth of mobile social networks. It attempts to identify a set of nodes, referred to as influencers, contributing to the spread of maximum information. In this article, we present the construction of social relation graph based on mobile communication data. And we propose a new centrality measure—activity degree to characterize the activity of nodes. By combining the local attributes of nodes and the behavioral characteristics of nodes to measure node activity degree, which can be used to evaluate the influence of users in mobile social networks, we introduce Susceptible‐Infected‐Susceptible model to simulate the dynamic spreading of information. We take advantage of the two indicators the degree centrality and the betweenness centrality to get a better ranking results. In comparison with spanning graph and initial graph, the results of comparison demonstrate that our algorithm has advantages in the scope of influence propagation.

Game Theory and Reinforcement Learning Based Secure Edge Caching in Mobile Social Networks

Edge caching has become one of promising technologies in mobile social networks (MSNs) to proximally provide popular contents for mobile users. However, since caching contents inevitably consume resources (e.g., power, bandwidth, storage, etc.), edge caching devices maybe selfish to cheat the content provider for earning service fees. In addition, due to the open access of edge caching devices, the edge caching service is vulnerable to various attacks, such as man-in-the-middle attack and content tamper attack, etc., resulting in the degradation of content delivery performance. To efficiently tackle the above problems, in this paper, we propose a secure edge caching scheme for the content provider and mobile users in MSNs. Specifically, we first develop a secure edge caching framework consisting of the content provider, multiple edge caching devices, and some mobile users. To motivate the participation of edge caching devices, Stackelberg game is exploited to model the interactions between the content provider and edge caching devices. The content provider serves as the game-leader to determine the payment strategy of secure caching service and each edge caching device is the game-follower to make the strategy on the quality of secure caching service. Especially, the zero payment mechanism is adopted to suppress the selfish behaviors of edge caching devices. Apart from this, for lack of the knowledge on interactions between the content provider and edge caching devices in dynamic network scenarios, we also employ the Q-leaning to derive the optimal payment strategy of the content provider and the security strategy of edge caching device. Extensive simulations are conducted, and results demonstrate that the proposed scheme can efficiently motivate edge caching devices to provide the content provider and mobile users with high-quality secure caching services.

Joint head selection and airtime allocation for data dissemination in mobile social networks

By forming a temporary group, users in mobile social networks (MSNs) can disseminate data to others in proximity with short-range communication technologies. However, due to user mobility, airtime available for users in the same group to disseminate data is limited. In addition, for practical consideration, a star network topology among users in the group is expected. For the former, unfair airtime allocation among the users will undermine their willingness to participate in MSNs. For the latter, a group head is required to connect other users. These two problems have to be properly addressed to enable real implementation and adoption of MSNs. To this aim, we propose a joint head selection and airtime allocation scheme for data dissemination within the group using Nash bargaining theory. Specifically, we consider two cases in terms of user preference on the data to be disseminated: a homogeneous case and a heterogeneous case. For each case, a Nash bargaining solution (NBS) based optimization problem is proposed. The existence of optimal solutions to the optimization problems is proved, which guarantees Pareto optimality and proportional fairness. Next, an algorithm that allows distributed implementation is introduced. Finally, numerical results are presented to evaluate the performance, validate intuitions and derive insights of the proposed scheme.

Privacy-Preserving Fine-Grained Data Retrieval Schemes for Mobile Social Networks

In this paper, we propose privacy-preserving fine-grained data retrieval schemes for mobile social networks (MSNs). The schemes enable users to retrieve data from other users who are interested in some topics related to a subject of interest. We define a subject to be a broad term that can cover many fine-grained topics, e.g., History can be a subject and World War I can be a topic. We consider centralized and decentralized network models. Our centralized scheme allows users to securely outsource data to a server such that the server matches the users who are interested in same topic(s) and have defined social attributes with privacy preservation. Searchable encryption scheme and a proposed cryptography construct are used to enable the server to match the topics and attributes without knowing any private information. By using the social attributes, users can prescribe the other users who can be connected to. We also propose a decentralized scheme that can be used when there is no connection to the server, i.e, shortage of Internet connectivity. The scheme leverages friends-of-friends relationship and transferable trust concept, where each user trusts his friends and the friends of friends. If a friend is not interested in the requested subject, he/she can link him/her to his/her friends without knowing the requested subject to preserve privacy. Our schemes use Bloom filters to store the topics of interest to reduce the storage and communication overhead. This is important because the number of fine-grained topics can be large. Different techniques to store the topics in the filter are proposed and investigated. Performance metrics are proposed and evaluated using real implementations. Our analysis and implementation results demonstrate that our schemes can preserve the privacy of the MSN users with high performance.

Research on the Association of Mobile Social Network Users Privacy Information Based on Big Data Analysis

The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications. The existing researches on user privacy protection in mobile social network mainly focus on privacy p... | Find, read and cite all the research you need on Tech Science Press

Multistage and Elastic Spam Detection in Mobile Social Networks through Deep Learning

While mobile social networks (MSNs) enrich people's lives, they also bring many security issues. Many attackers spread malicious URLs through MSNs, which causes serious threats to users' privacy and security. In order to provide users with a secure social environment, many researchers make great efforts. The majority of existing work is aimed at deploying a detection system on the server and classifying messages or users in MSNs through graph-based algorithms, machine learning or other methods. However, as a kind of instant messaging service, MSNs continually generate a large amount of user data. Without affecting the user experience, with existing detection mechanisms it is difficult to implement real-time detection in practical applications. In order to realize real-time message detection in MSNs, we can build more powerful server clusters or improve the utilization rate of computing resources. Assuming that computing resources of servers are limited, we use edge computing to improve the utilization rate of computing resources. In this article, we propose a multistage and elastic detection framework based on deep learning, which sets up a detection system at the mobile terminal and the server, respectively. Messages are first detected on the mobile terminal, and then the detection results are forwarded to the server along with the messages. We also design a detection queue, according to which the server can detect messages elastically when computing resources are limited, and more computing resources can be used for detecting more suspicious messages. We evaluate our detection framework on a Sina Weibo dataset. The results of the experiment show that our detection framework can improve the utilization rate of computing resources and can realize real-time detection with a high detection rate at a low false positive rate.

Privacy Leakage of Location Sharing in Mobile Social Networks: Attacks and Defense

Along with the popularity of mobile social networks (MSNs) is the increasing danger of privacy breaches due to user location exposures. In this work, we take an initial step towards quantifying location privacy leakage from MSNs by matching the users' shared locations with their real mobility traces. We conduct a three-week real-world experiment with 30 participants and discover that both direct location sharing (e.g., Weibo or Renren) and indirect location sharing (e.g., Wechat or Skout) can reveal a small percentage of users' real points of interests (POIs). We further propose a novel attack to allow an external adversary to infer the demographics (e.g., age, gender, education) after observing users' exposed location profiles. We implement such an attack in a large real-world dataset involving 22,843 mobile users. The experimental results show that the attacker can effectively predict demographic attributes about users with some shared locations. To resist such attacks, we propose SmartMask, a context-based system-level privacy protection solution, designed to automatically learn users' privacy preferences under different contexts and provide a transparent privacy control for MSN users. The effectiveness and efficiency of SmartMask have been well validated by extensive experiments.

Relay selection based on social relationship prediction and information leakage reduction for mobile social networks

Despite the extensive study on relay selection in mobile social networks (MSNs), few work has taken both transmission latency (i.e. efficiency) and information leakage probability (i.e. security) into consideration. Therefore we target on designing an efficient and secure relay selection algorithm to enable communication among legitimate users while reducing the information leakage probability to other users. In this paper, we propose a novel mobility model for MSN users considering both the randomness and the sociality of the movements, based on which the social relationship among users, i.e. the meeting probabilities among the users, are predicted. Taken both efficiency and security into consideration, we design a network formation game based relay selection algorithm by defining the payoff functions of the users, designing the game evolving rules, and proving the stability of the formed network structure. Extensive simulation is conducted to validate the performance of the relay selection algorithm by using both synthetic trace and real-world trace. The results show that our algorithm outperforms other algorithms by trading a balance between efficiency and security.

Large Scale Measurement and Analytics on Social Groups of Device-to-Device Sharing in Mobile Social Networks

Recently many studies demonstrate that exploiting the Device-to-Device (D2D) content sharing in offline Mobile Social Networks (MSNs) is a promising solution to offload cellular data to local connectivities in proximity to reduce the duplicated cellular transmissions via the backbone network demanded by nearby users and hence to improve users’ quality of service (QoS). However, related D2D-based social sharing and offloading proposals are based on either assumptions and theoretical models, or limited data-driven analysis caused by small scale of data sets (e.g. hundreds of MSN users) or single-dimensional feature (e.g. human mobility only), which severely restricts applications in practice. In this paper, we perform the large-scale measurement and analytics on D2D-based content sharing groups from the perspective of social networks via the platform of Xender, one of leading global D2D sharing platforms in Asia. This D2D sharing data is a precious offline social relationship that reflects the characteristics of large-scale offline users. We analyze the behaviors of about 40 million users with 443 million D2D transmissions of 17 million files in 865 thousand social groups, and unveil the details of social structure properties, network motifs, cascade trees of friendship and propagation which are helpful for improving social D2D sharing services.

Exploring Communication Behaviors of Users to Target Potential Users in Mobile Social Networks

In mobile communication services, users can communicate with each other over different telecommunication carriers. For telecom operators, how to acquire and retain users is a significant and practical task. Note that telecom operators only have their own customer profiles. For the users from other telecom operators, their information is sparse. Thus, given a set of communication logs, the main theme of our work is to identify the potential users who will possibly join the target services in the near future. Since only a limited amount of information is available, one challenging issue is how to extract features from the communication logs. In this article, we propose a Communication-Based Feature Generation (CBFG) framework that extracts features and builds models to infer the potential users. Explicitly, we construct a heterogeneous information network from the communication logs of users. Then, we extract the explicit features, which refer to those calling features of users, from the potential users’ interaction behaviors in the heterogeneous information network. Moreover, from the calling behaviors of users, one could extract the possible community structures of users. Based on the community structures, we further extract the implicit features of users. In light of both explicit and implicit features, we propose an information-gain-based method to select the effective features. According to the features selected, we utilize three popular classifiers (i.e., AdaBoost, Random Forest, and SVM) to build models to target the potential users. In addition, we have designed a sampling approach to extract training data for classifiers. To evaluate our methods, we have conducted experiments on a real dataset. The results of our experiments show that the features extracted by our proposed method can be effective for targeting the potential users.

Factors Affecting Loyalty of Mobile Social Networks' Users

Increasing world-wide trends of using mobile social networks and the rise of competition between different social applications makes it essential for social network providers and marketers to identify the key factors leading to user loyalty. The purpose of this paper is to identify the key factors that affect the loyalty of mobile social networks users. The proposed model was tested through structural equation modeling techniques and an online survey. The sample consisted of 388 mobile social networks users in Iran. The results indicate that sociability, entertainment and fashion are primary drivers of attitude toward a mobile social network. The results also show the significant role of attitude and satisfaction on consumer loyalty. This study helps both marketers and mobile social network providers know the key drivers of customer loyalty in order to tailor their marketing efforts and communication strategies.

Mobile Social Networking Under Side-Channel Attacks: Practical Security Challenges

Mobile social networks (MSNs) are the networks of individuals with similar interests connected to each other through their mobile devices. Recently, MSNs are proliferating fast supported by emerging wireless technologies that allow to achieve more efficient communication and better networking performance across the key parameters, such as lower delay, higher data rate, and better coverage. At the same time, most of the MSN users do not fully recognize the importance of security on their handheld mobile devices. Due to this fact, multiple attacks aimed at capturing personal information and sensitive user data become a growing concern, fueled by the avalanche of new MSN applications and services. Therefore, the goal of this work is to understand whether the contemporary user equipment is susceptible to compromising its sensitive information to the attackers. As an example, various information security algorithms implemented in modern smartphones are thus tested to attempt the extraction of the said private data based on the traces registered with inexpensive contemporary audio cards. Our obtained results indicate that the sampling frequency, which constitutes the strongest limitation of the off-the-shelf side-channel attack equipment, only delivers low-informative traces. However, the success chances to recover sensitive data stored within a mobile device may increase significantly when utilizing more efficient analytical techniques as well as employing more complex attack equipment. Finally, we elaborate on the possible utilization of neural networks to improve the corresponding encrypted data extraction process, while the latter part of this paper outlines solutions and practical recommendations to protect from malicious side-channel attacks and keep the personal user information protected.

Social influence modeling using information theory in mobile social networks

Social influence analysis has become one of the most important technologies in modern information and service industries. Thus, how to measure social influence of one user on other users in a mobile social network is also becoming increasingly important. It is helpful to identify the influential users in mobile social networks, and also helpful to provide important insights into the design of social platforms and applications. However, social influence modeling is an open and challenging issue, and most evaluation models are focused on online social networks, but fail to characterize indirect influence. In this paper, we present a mechanism to quantitatively measure social influence in mobile social networks. We exploit the graph theory to construct a social relationship graph that establishes a solid foundation for the basic understandings of social influence. We present an evaluation model to measure both direct and indirect influence based on the social relationship graph, by introducing friend entropy and interaction frequency entropy to describe the complexity and uncertainty of social influence. Based on the epidemic model, we design an algorithm to characterize propagation dynamics process of social influence, and to evaluate the performance of our solution by using a customized program on the basis of a real-world SMS/MMS-based communication data set. The real world numerical simulations and analysis show that the proposed influence evaluation strategies can characterize the social influence of mobile social networks effectively and efficiently.

Multi‐dimensional fuzzy trust evaluation for mobile social networks based on dynamic community structures

SummaryAs mobile social networks (MSNs) are booming and gaining tremendous popularity, there have been an increasing number of communications and interactions among users. Taking this advantage, users in MSNs make decisions via collecting and combining trust information from different users. Hence, trust evaluation technology has become a key requirement for network security in MSNs. In such MSNs, however, the community/group structures are dynamically changing, and users may belong to multiple communities/groups. Therefore, trust evaluation plays a critical role in inferring trustworthy contacts among users. In this paper, an innovative trust inference model is proposed for MSNs, in which multiple dimensional trust metrics are incorporated to reflect the complexity of trust. To infer trust relations between users in MSNs with complex communities, we first construct dynamic implicit social behavioral graphs (DynISBG) based on dynamic complex community/group structures and propose an efficient detection algorithm for DynISBG under fuzzy degree κ. We then present a multi‐dimensional fuzzy trust inferring approach that involves four metrics, that is, static attribute trust factor, dynamic behavioral trust factor, long‐term trust evolution factor, and recommendation‐based trust opinion. Moreover, to obtain the recommendation‐based trust opinion about indirect connected users, we discuss the trust aggregation and propagation along trust path. Finally, we evaluate the performance of our novel approach with simulations. The results show that, compared with the existing approaches, the proposed model provides a more detailed analysis in trust evaluation with higher accuracy. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Social-aware data dissemination service in mobile social network with controlled overhead

The popularity of Mobile Social Networks (MSNs) paves a way for mobile users to download or view their interested contents not only from access points of Internet but also from other mobile users through short-range radio communications like Bluetooth and Wi-Fi. Through communications between users in MSNs, the bandwidth of short-range communications can be better utilized and relieve the stress on the bandwidth of cellular network. However, due to intermittent connectivity in MSNs, nodes in MSNs can only forward messages opportunistically. Therefore, how to disseminate data accurately and efficiently turns out as a challenging problem. Most previous works focus more on increasing delivery ratio while paying less attention on reducing overhead measures the number of copies that are stored and spread by relay nodes that are not interested at all in the contents. Note that high data dissemination ratio is usually accompanied by high and uncontrolled overhead, resulting in heavy storage burden on the nodes in MSNs and low quality of data dissemination. This motivates us to propose a new data dissemination scheme to maximize data dissemination ratio with controlled overhead. And more specifically, with given limitation on the overhead of spreading the messages, we design a new data dissemination scheme to maximize data dissemination ratio. In our data dissemination scheme, a time-homogeneous Markov model is designed to analyze the interest transitions of every node’s neighbors to decide when to forward the message and which node is chosen to forward the message further. Furthermore, two utility functions are investigated to evaluate the service ability of nodes for each kind of interest among the messages. The experimental results on simulated and real datasets demonstrate that our new scheme can outperform existing protocols with higher delivery ratio and lower delivery latency under controlled overheads.