PeGraph: A System for Privacy-Preserving and Efficient Search Over Encrypted Social Graphs

In the social media era, the ever-increasing utility of Online Social Networks (OSN) services provide a variety of benefits to users, organizations, and service providers. However, OSN services also introduce new threats and privacy issues regarding the data they are dealing with. For instance, in a reliable OSN service, a user should be able to set up his desired level of information sharing and securely manage sensitive data. Currently, few approaches exist that can model OSNs for the purpose, let alone a model the effects that attackers can have on these networks. In this work a novel OSN modeling approach is presented to fill the gap. This model is based on an innovative game-theoretic approach and it is analyzed both from a theoretical and simulation-oriented view. The game-theoretic model is implemented to analyze several attack scenarios. Honeytokens, which are an information security tool based upon deception, are defined and identified as a security tool that could help in OSN security. As the results show, there are several scenarios where OSN services are very vulnerable and hence more protection mechanisms should be provided to secure the data contained across these networks, including the use of honeytokens. In this work we introduce a novel OSN modeling approach for optimal data sharing based on innovative game theories, considering the states/optimal policies of data sharing on OSNs and possible confrontations between the attacker and the user. After we develop the theoretical framework, we conduct experiments, integrating our ideas with honeytokens in several attack scenarios. Finally, we analyze our experimental results and discuss recommendations based on the results.

Online social networks—Paradise of computer viruses

The popularization of Information and Communication Technologies allowed new forms of interaction, including Online Social Network (OSN) services. These services could share personal data with third-party organizations through Application Programming Interfaces (API), making it a complex task to observe the data flow. This research proposes a model to identify levels and elements available in the flow of personal data via API between organizations holding OSN services and external agents. It converged the recurrent mechanisms into providing data sets from the OSN services to external agents from a systematized reading of the technical documentation to establish a database model to fetch unitedly the possibility of storing information about the levels in which API provided personal data to external agents, applying the Entity-Relationship Model. Subsequently, it presents a Data Mart as a proof of concept for the proposed database model, intending to compare the ways of accessing the personal data attributes stored and, following, shared by the OSN services to external agents. It showed that the model could clarify the elements inherent in the data flow, allowing a more structured analysis, including the possibility of monitoring changes in the API by organizations over time.

Online Social Networks (OSN) have become an integral part of our everyday lives. We express ourselves, share content and information with our friends and colleagues, or exchange messages. Yet, despite social communication being implicitly a distributed, decentralized way of information exchange that does not require any central entity to supervise the process, most OSN services are built in a central, monolithic fashion. This contradicts the idea of the social web, as proprietary and isolated walled gardens keep users from being able to freely choose an OSN platform provider or to effectively control their privacy. To enable users of the social web to remain in control of their social profiles and data, solutions were proposed that allow hosting one's social profile on independent servers, which then connect to each other in a loosely coupled fashion. Unfortunately, implicit network effects existing in large OSN services prevent users from migrating to alternative solutions thus ultimately hindering alternative OSN services to succeed in the social web. In this paper, we outline the main challenges that need to be addressed in order to allow decentralized OSN services to become real alternatives to well-established services such as Facebook or Twitter. The identified main challenges are coverage of typical, standard OSN functionality, a holistic and extensible interoperability protocol, support for data portability of entire social profiles, and support for platform-independent user identification. The paper then outlines Sonic, an open architecture for an open and heterogeneous network of independent OSN services, the Online Social Network Federation (OSNF), in which the identified challenges are addressed altogether.

These proceedings contain the papers selected for presentation at the seventh edition of the ACM SIGSPATIAL Workshop on Location-Based Social Networks (LBSN 2014) which is being held in conjunction with the ACM SIGSPATIAL GIS conference in 2014. Social networks have been prevalent on the Internet and become a hot research topic attracting many professionals from a variety of fields. The advances in location-acquisition and mobile communication technologies empower people to use location data with existing online social networks. The dimension of location helps bridge the gap between the physical world and online social networking services. Furthermore, people in an existing social network can expand their social structure with the new interdependency derived from their locations. As location is one of the most important components of user context, extensive knowledge about an individual's interests, behaviors, and relationships with others can be learned from locations. These kinds of location-embedded and location-driven social structures are known as location-based social networks. The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of LBSN development and applications, present their ideas and contributions, and set future directions in emerging innovative research for location based social networks. This year program is composed of two tracks of predominantly "social" and "spatial" views on LBSNs.

The privacy policies of online social network (OSN) service providers are criticised as falling short of satisfying their users' privacy expectations letting huge quantities of their personally identifiable information (PII) exposed to unknown audiences. The purpose of this paper is twofold: to assess the conformance of the privacy policies applied in the five topmost leading OSNs to an internationally acknowledged benchmark such as the ISO 29100:2011 standard, and to propose improvements based on the findings of the assessment. Further, as serious mismatches between these privacy policies and the adherence criteria set out in the ISO 29100:2011 standard were identified, a data lifecycle model is proposed as the basis for an improved OSN privacy policy. A restructuring of the existing policies according to the data lifecycle model will allow them to enjoy characteristics that are known to be important in forming users' perceptions.

Online social networking services are hot in the recent years, but most of theses services in China aimed at the college students and business people, ignoring that Chinese migrant workers could be one of the biggest potential user groups of mobile social networking services in the future. This paper presents an ethnographic user study on Chinese rural-urban migrant workers' social networks in daily life and proposes a new mobile social networking service concept design for helping them to improve their social networks in daily life.

Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors. Traditionally, the unavailability of large-scale and representative data on social connectedness between individuals or geographic regions has posed a challenge for empirical research on social networks. More recently, a body of such research has begun to emerge using data on social connectedness from online social networking services such as Facebook, LinkedIn, and Twitter. To date, most of these research projects have been built on anonymized administrative microdata from Facebook, typically by working with coauthor teams that include Facebook employees. However, there is an inherent limit to the number of researchers that will be able to work with social network data through such collaborations. In this paper, we therefore introduce a new measure of social connectedness at the US county level. Our Social Connectedness Index is based on friendship links on Facebook, the global online social networking service. Specifically, the Social Connectedness Index corresponds to the relative frequency of Facebook friendship links between every county-pair in the United States, and between every US county and every foreign country. Given Facebook’s scale as well as the relative representativeness of Facebook’s user body, these data provide the first comprehensive measure of friendship networks at a national level.

The increase in use of Smart mobile devices has allowed for an ever growing market for services providers. These services are increasingly used to connect to users' on-line private information through social networking sites that share and personalise on-line information. This leads to the problem of privacy leaks stemming from an application's non-adherence to a predefined set of privacy policies. This paper presents a formal methods tool to reliably restrict the access to content in on-line social network services. The Poporo tool builds upon a previous work in which we provided a predicate calculus definition for social networking in B that models social-network content, privacy policies, and social-network friendship relations. This paper presents the implementation and the functionality of our Poporo tool through a running example in the domain of social networking sites.

Thanks to online social networks services such as Facebook, Twitter, YouTube, etc.…, users have the opportunity to communicate easily without constraints by providing them with free and open platforms. Because of their crucial role in spreading information between users, these platforms have become an integral part of everyday life. Privacy and security are major concerns for many social media users. When users share information (such as data, photos etc.…) with their friends, they are not always safe; they can make their friends easy targets attacked by malicious users who try with multiple methods to access their personal data. Unfortunately, with the continued expansion of a user’s social network, privacy settings alone are often insufficient to protect the user’s profile. In this paper, we have proposed a new approach to limit the spread and dissemination of spam content and malicious profiles in social networks. In another way, prohibit the creation of malicious profiles by automated robots as well as the falsification of identity when publishing spam content in social networks. Given that when using online social networking services, users trust them to process their information; have seen that it is necessary not to neglect this part; but rather to make a set of changes that will be consistent with our proposal to strengthen the protection of personal data of users of these platforms.

EPA-1595 - Factors affecting negative emotions of depression, anxiety and stress in online social networking services

With the development of mobile Internet, more and more individuals and institutions tend to express their views on certain things (such as software and music) on social platforms. In some online social network services, users are allowed to label users with similar interests as “trust” to get the information they want and use “distrust” to label users with opposite interests to avoid browsing content they do not want to see. The networks containing such trust relationships and distrust relationships are named signed social networks (SSNs), and some real-world complex systems can be also modeled with signed networks. However, the sparse social relationships seriously hinder the expansion of users’ social circle in social networks. In order to solve this problem, researchers have done a lot of research on link prediction. Although these studies have been proved to be effective in the unsigned social network, the prediction of trust and distrust in SSN has not achieved good results. In addition, the existing link prediction research does not consider the needs of user privacy protection, so most of them do not add privacy protection measures. To solve these problems, we propose a trust-based missing link prediction method (TMLP). First, we use the simhash method to create a hash index for each user. Then, we calculate the Hamming distance between the two users to determine whether they can establish a new social relationship. Finally, we use the fuzzy computing model to determine the type of their new social relationship (e.g., trust or distrust). In the paper, we gradually explain our method through a case study and prove our method’s feasibility.

The social network is a social structure made up of individuals who are tied by social links. With the rapid development of information technology, online social networking services and microblogging service received a lot of attention. Social networks provide a comprehensive communication platform of interaction, knowledge sharing, information dissemination to people, etc. They also bring a significant impact on people’s working style and interpersonal communication. Drawing from trait theory, regulatory focus theory, followership theory, political skills, self-construal theory, and performance theory, this study systematically investigates the antecedents that induce the difference in followership behavior and the different consequences of behavior on job performance. We introduce a novel hybrid similarity measure, and the best matching based supervised learning process is conducted for training the time series. The events before the current timestamp can be adopted as a training set, and an early predictor will be generated by learning the rules from the training set. The newly coming events will be used for verifying the predictor, or assessing and tuning it. This paper clarifies the antecedents’ mechanism for differences in followership behavior and the consequence mechanism that followership behavior differently impacts job performance.

Sampling of large social graphs is used for addressing infeasibility of measurements in large social graphs, or for crawling graphs from online social network services where accessing an entire social graph at once is often impossible. Sampling algorithms aim at maintaining certain properties of the original graphs in the sampled (or crawled) ones. Several sampling algorithms, such as breadth-first search, standard random walk, and Metropolis-Hastings random walk, among others, are widely used in the literature for sampling graphs. Some of these sampling algorithms are known for their bias, mainly towards high degree nodes, while bias for other metrics is not well-studied. In this paper we consider the bias of sampling algorithms on the mixing time. We quantitatively show that some existing sampling algorithms, even those which are unbiased to the degree distribution, always produce biased estimation of the mixing time of social graphs. We argue that bias in sampling algorithms accepted in the literature is rather metric-dependent, and a given sampling algorithm, while may work nicely and unbiased to one property, may produce considerable amount of bias in other properties.

In some online Social Network Services, users are allowed to label their relationship with others, which can be represented as links with signed values. The link prediction problem is to estimate the values of unknown links by the information from the social network. A lot of similarity based metrics and machine learning based methods are proposed. Most of these methods are based on the network topological and node states. In this paper, by considering the information from user comment and sentiment lexicon, our methods improved the performances of link prediction for both similarity based metrics and machine learning based methods.KeywordsLink predictionSocial networksLink network structureUser commentsSentiment lexicon