Mobile Online Social Networks Research Articles

ASPA-MOSN: An Efficient User Authentication Scheme for Phishing Attack Detection in Mobile Online Social Networks

Over the last few years, with the massive growth of smartphone technology and mobile Internet, the use of various online social networks (OSNs) have increased rapidly. This ever-growing use of social networks leverages cyber-attackers to exploit various phishing schemes, spoofed accounts, and other threats to steal users’ credentials. Phishing is an online crime that employs both technical subterfuge and social engineering to steal consumers’ personal identity, financial account credentials, and other sensitive information. In general, a phishing attack is carried out by the exercise of sending fraudulent communications (like a fake email with harmful uniform resource locators), that pretends to come from a reputable source. The problem of designing user authentication protocol for mitigating phishing attacks in OSNs is a challenging research problem. In this article, we propose a secure and lightweight cryptography-based authentication scheme, called authentication scheme for phishing attack (ASPA)-mobile online social network (mOSN), that provides resistance to phishing and other related attacks in OSNs. The security of the proposed scheme is explained using both informal security analysis and formal security analysis through the widely recognized real-or-random model and ProVerif simulation tool. Finally, we compare the security, functionality, computation, and communication costs of the proposed ASPA-mOSN with related schemes. The comparison results show that ASPA-mOSN outperforms other existing competing schemes.

Location-sharing protocol for privacy protection in mobile online social networks

Location-based services are becoming more and more popular in mobile online social networks (mOSNs) for smart cities, but users’ privacy also has aroused widespread concern, such as locations, friend sets and other private information. At present, many protocols have been proposed, but these protocols are inefficient and ignore some security risks. In the paper, we present a new location-sharing protocol, which solves two issues by using symmetric/asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one location server; connecting social network server and location server directly instead of through cellular towers; avoiding broadcast encryption. We introduce dummy identities to protect users’ identity privacy, and prevent location server from inferring users’ activity tracks by updating dummy identities in time. The details of security and performance analysis with related protocols show that our protocol enjoys two advantages: (1) it’s more efficient than related protocols, which greatly reduces the computation and communication costs; (2) it satisfies all security goals; however, most previous protocols only meet some security goals.

Building Social Capital in Higher Education With Online Opportunistic Social Matching

Recently, colleges and universities have been forced to utilize distance learning. With students spending less time on campus, their sense of community may decrease because they would be less likely to participate in the community. This puts higher education commuter institutions at a disadvantage in terms of generating and maintaining social capital. The authors investigate the possibility to counter this disadvantage by actively promoting participation in a mobile online social network (OSN) supported by a context-aware notification and recommender system (NARS) to achieve opportunistic social matching, which mitigates information overload by considering each user's relational, social, and personal context as predictors of match opportunities. The results suggest that introducing a purposefully designed OSN has the potential to facilitate the creation of structural and relational social capital, but that it might not have an effect on cognitive social capital.

A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks

With the advent of intelligent handheld devices, location sharing becomes one of the most popular services in mobile online social networks (mOSNs). In location-sharing services, users can enjoy a better social experience by updating their real-time location information. However, the leakage of private information may hinder the further development of location-sharing services. Although many solutions have been proposed to protect users’ privacy, the privacy-utility trade-offs must be considered. Therefore, we propose a new scheme called search efficient privacy-preserving location-sharing (SELS) system. In our scheme, we create a new approach named associated grids to improve the efficiency of location-sharing systems while maintaining users’ privacy. In addition, by setting the user-defined access control policy proposed in our scheme, users’ flexible privacy-preserving requirements can be satisfied. Detailed complexity and security analysis show that the proposed scheme is a practical and efficient privacy-preserving solution. Extensive simulations are performed to validate the effectiveness and performance of our scheme.

Privacy Enhanced Location Sharing for Mobile Online Social Networks

As a primitive function of location-based services (LBSs), the location sharing aims to provide a user's current location information to other designated users. In recent years, LBSs have become one of the most popular services provided by mobile online social networks (mOSNs). As LBSs actively exploit the users’ identity and current location information, appropriate approaches have to be utilized to protect the location privacy of the users. Several recent reports have discussed the significance of friendship privacy protection with the goal of hiding the friendship relation of users from unintended entities. However, to the best of our knowledge, there hasn't been an approach for protecting the location sharing with complete privacy of location and friendship connections. To address this issue, we propose a new cryptographic primitive, functional pseudonym, for location sharing in mOSNs that ensures both of them. Unlike many of the existing solutions, our approach does not require a fully trusted server and does not assume pre-established secrets among friends, and therefore is highly practical. Also, the proposed approach significantly reduces computational overhead of users by delegating part of the computations for location sharing to a server, therefore it is highly sustainable. Our primitive can be widely used in many mOSNs to enable LBSs with improved privacy and sustainability. Consequently, it will contribute to proliferate LBSs by eliminating users privacy concerns.

PPLS: a privacy-preserving location-sharing scheme in mobile online social networks

The recent proliferation of mobile devices has given rise to mobile online social networks (mOSNs), an emerging network paradigm that uses social networks as its main design element. As one of the most critical components in mOSNs, location sharing plays an important role in helping users share information and strengthen their social bonds, which however may compromise users privacy, including location information and social relationship details. To address these challenges, some solutions have been proposed. However, none of them considers the privacy of inter-user threshold distance, which effectively can be used to identify users, their friends, and location information, by malicious or undesired elements of the system. To overcome this limitation, we propose a secure distance comparison protocol. Furthermore, we present a privacy-preserving location-sharing scheme (PPLS), which allows users to build more complex access control policies. The safety of our scheme is validated by the security analysis and the experimental results demonstrate the efficiency of PPLS scheme.

A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications

The rapid development of mobile internet technology and the better availability of GPS have made mobile online social networks (mOSNs) more popular than traditional online social networks (OSNs) over the last few years. They necessitate fundamental social operations such as establishing friend relationship, location sharing among friends, and providing location-based services. As a consequence, security and privacy issues demands the utmost importance to mOSNs users. The first stream of existing solutions adopts two different servers to store locations-based and social network-based information separately, thereby sustaining large storage and communication overhead. The second stream of solutions aims at integrating the social network server and the location-based server into a single entity. However, as these approaches exploit only one single server, they may face several performance issues related to server bottlenecks. Moreover, such schemes are found to be vulnerable to various active and passive security attacks. In this paper, we propose a privacy preserving, secure and efficient location sharing scheme for mOSNs, which shows both efficiency and flexibility in the location update, sharing, and query of social friends and social strangers. The security of the proposed scheme is validated using random oracle based formal security proof and Burrows-Abadi-Needham (BAN) logic based authentication proof, followed by informal security analysis. Additionally, we have used ProVerif 1.93 to verify the security of the system. The efficiency and practicability of the proposed scheme are demonstrated through experimental implementation and evaluation.

An investigation into the requirements of privacy in social networks and factors contributing to users’ concerns about violation of their privacy

Social networks are specific types of social media which consolidate the ability of omnipresent connection for users and devices to share user-centric data objects among interested users. Taking advantage of the characteristics of both mobile social networks (MSNs) and online social networks (OSNs), MSNs are capable of providing an efficient and effective mobile environment for users to access, distribute, and share data. OSNs provide capability of search, data sharing, and online social interactions for users through Internet sites. However, the lack of a protective infrastructure in these networks has turned them into convenient targets for various risks. This is the main purpose why social networks including MSNs and OSNs carry disparate and intricate safety concerns specially privacy-preserving challenges and what has been done to improve these challenges. In addition, what types of data should be protected and what are the different architectures provided for each of these networks? In this paper, we aim to provide a clear categorization on privacy challenges and a deep exploration over some recent solutions in MSNs and OSNs. In particular, in MSNs, proposed scheme to protect data types is categorized, and in OSNs, all types of proposed architectures, along with the proposed mechanisms for privacy, are classified. To conclude, several major open research issues are discussed, and future research directions are outlined.

Verifiable mobile online social network privacy‐preserving location sharing scheme

SummaryWith the dramatic growth of smart phones and social network services, location‐based service has become increasingly popular in our daily life, which also brings great privacy leakage about user's location information. Thus, privacy‐preserving location sharing is still a significant problem. In this paper, two models of our scheme are verifiable, supporting location query on homomorphic encrypted ciphertext with searching index and trapdoor. The characteristics of our scheme are that it (1) allows users to share their current location without leaking any location information to the untrusted server; (2) protects users from the cheating of untrusted server's malicious behavior by verifying query result; (3) supports a novel personalized query according to users' classifications of social network friends; (4) the user does not have to be online to make permissions for accessing his shared location, and (5) model 2 does not rely on any trusted third party server to perform any computation. The security analysis shows that it ensures secure location sharing in social scenarios.

Socially-Driven Learning-Based Prefetching in Mobile Online Social Networks

Mobile online social networks (OSNs) are emerging as the popular mainstream platform for information and content sharing among people. In order to provide the quality of experience support for mobile OSN services, in this paper, we propose a socially-driven learning-based framework, namely Spice, for the media content prefetching to reduce the access delay and enhance mobile user's satisfaction. Through a large-scale data-driven analysis over real-life mobile Twitter traces from over 17 000 users during a period of five months, we reveal that the social friendship has a great impact on user's media content click behavior. To capture this effect, we conduct the social friendship clustering over the set of user's friends, and then develop a cluster-based Latent Bias Model for socially-driven learning-based prefetching prediction. We then propose a usage-adaptive prefetching scheduling scheme by taking into account that different users may possess heterogeneous patterns in the mobile OSN app usage. We comprehensively evaluate the performance of Spice framework using trace-driven emulations on smartphones. Evaluation results corroborate that the Spice can achieve superior performance, with an average 80.6% access delay reduction at the low cost of cellular data and energy consumption. Furthermore, by enabling users to offload their machine learning procedures to a cloud server, our design can achieve up to a factor of 1000 speed-up over the local data training execution on smartphones.

Location-Sharing Systems With Enhanced Privacy in Mobile Online Social Networks

Location sharing is one of the critical components in mobile online social networks (mOSNs), which has attracted much attention recently. With the advent of mOSNs, more and more users' location information will be collected by the service providers in mOSN. However, the users' privacy, including location privacy and social network privacy, cannot be guaranteed in the previous work without the trust assumption on the service providers. In this paper, aiming at achieving enhanced privacy against the insider attack launched by the service providers in mOSNs, we introduce a new architecture with multiple location servers for the first time and propose a secure solution supporting location sharing among friends and strangers in location-based applications. In our construction, the user's friend set in each friends' query submitted to the location servers is divided into multiple subsets by the social network server randomly. Each location server can only get a subset of friends, instead of the whole friends' set of the user as the previous work. In addition, for the first time, we propose a location-sharing construction which provides checkability of the searching results returned from location servers in an efficient way. We also prove that the new construction is secure under the stronger security model with enhanced privacy. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.

Propagation- and Mobility-Aware D2D Social Content Replication

Mobile online social network services have seen rapid expansion; thus, the corresponding huge amounts of user-generated social media contents propagating between users via social connections have significantly challenged the traditional content delivery paradigm. First, replicating all the contents generated by users to edge servers that well “fit” the receivers becomes difficult due to limited bandwidth and storage capacities. Motivated by device-to-device (D2D) communication, which allows users with smart devices to transfer content directly, we propose replicating bandwidth-intensive social contents in a device-to-device manner. Based on large-scale measurement studies on social content propagation and user mobility patterns in edge-network regions, we observe the following: (1) Device-to-device replication can significantly help users download social contents from neighboring peers. (2) Both social propagation and mobility patterns affect how contents should be replicated. (3) The replication strategies depend on regional characteristics (e.g., how users move across regions). Using these measurement insights, we propose a propagation- and mobility-aware content replication strategy for edge-network regions, in which social contents are assigned to users in edge-network regions according to a joint consideration of social graphs, content propagation, and user mobility. We formulate the replication scheduling as an optimization problem and design a distributed algorithm using only historical, local, and partial information to solve it. Trace-driven experiments further verify the superiority of our proposal: compared with conventional pure-movement-based and popularity-based approaches, our design can significantly improve ( $2-4$ -fold improvement) the amount of social content successfully delivered via device-to-device replication.

CenLocShare: A centralized privacy-preserving location-sharing system for mobile online social networks

Friend relationships and location information are the biggest privacy concerns for the users in mobile Online Social Networks (mOSNs). Existing solutions save these two kinds of privacy information in two different servers, i.e., Social Network Server (SNS) and Location Based Server (LBS), and employ some encryption protocols to map each other to provide the location-sharing service among friends and strangers. However, this distributed architecture incurs large communication overhead between SNS and LBS, and great storage burden as well. In order to overcome these shortages, we propose a centralized privacy-preserving location-sharing system, named CenLocShare. It integrates SNS and LBS into one server, i.e., Location-storing Social Network Server (LSSNS), and uses the dummy locations and the dedicated mapping protocols between LSSNS and Cellular Tower (CT) to share privacy-preserving locations. Its safety is validated by the security analysis. Furthermore, we implement the prototype of CenLocShare and do some comparisons with other systems. The thorough experiments indicate our system decreases the query time for friends’ locations and the storage space CenLocShare is more suitable and effective in the context of mOSNs

User-defined privacy location-sharing system in mobile online social networks

With the fusion of social networks and location-based services, location sharing is one of the most important services in mobile online social networks (mOSNs). In location-sharing services, users have to provide their location information to service provider. However, location information is sensitive to users, which may cause a privacy-preserving issue needs to be solved. In the existing research, location-sharing services, such as friends’ query, does not consider the attacks from friends. In fact, a user may not trust all of his/her friends, so just a part of his/her friends will be allowed to obtain the user's location information. In addition, users’ location privacy and social network privacy should be guaranteed. In order to solve the above problems, we propose a new architecture and a new scheme called User-Defined Privacy Location-Sharing (UDPLS) system for mOSNs. In our scheme, the query time is almost irrelevant to the number of friends. We also evaluate the performance and validate the correctness of our proposed algorithm through extensive simulations.

An efficient and privacy-preserving location sharing mechanism

The rise of Internet of Things has been improving the so-called mobile Online Social Networks (mOSNs) in sense of more ubiquitous inter-communication and information sharing. Meanwhile, location sharing service is known as the key cornerstone of mOSNs. Unfortunately, location sharing has also caused similarly serious concerns on the potential privacy leakage. We propose BMobishare, a security enhanced privacy-preserving location sharing mechanism. It employs the Bloom Filter to mask sensitive data exchanges, such that exchange of both sides cannot obtain unauthorized privacy information. Analyses and evaluations show that BMobishare's enhanced location sharing procedure achieves significantly better performance when compared to existing approaches.

QoE-Based Server Selection for Content Distribution Networks

As current server capacity and network bandwidth become increasingly overloaded by the rapid growth of high quality emerging multimedia services such as mobile online gaming, social networking or IPTV, a critical factor of success of these multimedia services becomes the end-user perception of quality while them using the service. As a result, user-centered approaches that consider quality of experience (QoE) constitute the current design trend for network systems of content providers and network operators. A content distribution network (CDN) that replicates the content from original servers to the replicated servers close to end users is actually an effective solution to improve network quality. We propose a QoE-based server selection algorithm in the context of a CDN architecture. Using realistic characteristics of the server selection process, we formalize our selection model as a sequential decision problem solved by the multi-armed bandit (MAB) paradigm. By using realistic experiments, we demonstrate that our approach yields significant improvements in term of user perception compared to traditional methods (such as Fastest, Closest and Round Robin).

Enabling informal digital guanxi for rural doctors in Shaanxi, China

In China, rural healthcare systems have been neglected in favor of the development of market-driven, largely urban health information systems (HIS). We investigated the effective use of information and communication technologies (ICTs) to develop a healthcare model within the rural healthcare system. We investigated information needs, existing healthcare structure, and the use of informal networks – guanxi – to acquire health information mediated by technology. Qualitative interviews (N = 74) were conducted in Shaanxi province of China. Respondents included village doctors (30), rural patients (10), urban doctors (17), and 17 other healthcare stakeholders. We found benefits of the use of ICT in informal networks for village doctors, including the facilitation of communication, greater time efficiency, and better access to medical information, particularly in mobile phone and QQ online social network usage. Guanxi relationships were established at proximate, distant, and virtual levels. Emergent communicative practices, such as Internet-driven medical research by village doctors, led to negotiations in established relationships. As suggested by the theoretical model, infrastructural, economic, socio-cultural, and technological vulnerabilities were also observed. We caution against the drawbacks of replication of the closed guanxi system in the digital ICT healthcare arena, and discuss the implications for research and policy-making.

Sign Inference for Dynamic Signed Networks via Dictionary Learning

Mobile online social network (mOSN) is a burgeoning research area. However, most existing works referring to mOSNs deal with static network structures and simply encode whether relationships among entities exist or not. In contrast, relationships in signed mOSNs can be positive or negative and may be changed with time and locations. Applying certain global characteristics of social balance, in this paper, we aim to infer the unknown relationships in dynamic signed mOSNs and formulate this sign inference problem as a low-rank matrix estimation problem. Specifically, motivated by the Singular Value Thresholding (SVT) algorithm, a compact dictionary is selected from the observed dataset. Based on this compact dictionary, the relationships in the dynamic signed mOSNs are estimated via solving the formulated problem. Furthermore, the estimation accuracy is improved by employing a dictionary self-updating mechanism.