PRIVACY AND ONLINE SOCIAL NETWORK

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.

With the widespread adoption of cloud computing, it is increasingly popular for online social network (OSN) service providers to leverage the public cloud as a back-end to manage their services for the cloud’s well-understood benefits. However, the cloud is also notoriously subject to a wide attack surface, making it an imperative need to embed security in the cloud-backed OSN service from the very beginning. In light of this, in this paper, we design, implement, and evaluate PeGraph, the first system simultaneously allowing private, efficient, and rich queries over encrypted social graphs. PeGraph is aimed at safeguarding the confidentiality of the social graph at the cloud, while preserving the functionality of social search, a key enabler for quality OSN services like friend discovery and user targeting. PeGraph is built from a delicate synergy of insights from social graph modelling and lightweight cryptography such as searchable encryption and additive secret sharing, supporting rich social search queries like exact queries, fuzzy queries, and mixed queries. PeGraph also allows the cloud to obliviously render the encrypted social search results in a ranked order according to their importance, as per users’ preferences. Extensive experiments demonstrate that PeGraph can securely process a wide range of practical social search queries within 1 second, over a real-world social graph consisting of millions of entities.

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.

Online social networks—Paradise of computer viruses

Relationship between the level of intimacy and lurking in online social network services

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.

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.

Online social networking services in the management of patients with diabetes mellitus: Systematic review and meta-analysis of randomised controlled trials

This paper investigates cache placement on a cooperative cache built from individual client caches in an online social network or web service. We use a service that maintains a mapping between content and the clients that cache it, and propose cache placement schemes that leverage relationships between clients (for example, social links) and workload statistics, proactively placing content on clients that are likely to access it. We evaluate efficacy through simulation, comparing our schemes against commonly used cache placement algorithms as well as optimal placement. We synthesize a workload to match characteristics of online social networks. Simulation results of our proposed caching schemes impose moderate network overhead and show considerable improvement to the client's cache hit ratio, even under churn.

Today, Online Social Network (OSN) services have evolved as the highest-growing medium, enabling various online activities on one platform. However, they also bring new threats and privacy issues to the community. In a reliable OSN service, a user should be able to set up his or her desired level of information sharing with a certain group of other users. Unfortunately, there are attackers that may attempt to expose OSN users' private information or conceal the information that the user would like to share with other users. Therefore, in this paper, we propose a game theoretic approach for helping OSN users determine their optimum policy on OSNs in terms of data sharing, based on a two-player (i.e., agent and opponent) zero-sum Markov game model.

The online social network services have been growing rapidly over the past few years, and the social network services can easily obtain the locations of users with the recent increasing popularity of the GPS enabled mobile device. In the social network, calculating the similarity between users is an important issue. The user similarity has significant impacts to users, communities and service providers by helping them acquire suitable information effectively. There are numerous factors such as the location, the interest and the gender to calculate the user similarity. The location becomes a very important factor among them, since nowadays the social network services are highly coupled with the mobile device which the user holds all the time. There have been several researches on calculating the user similarity. However, most of them did not consider the location. Even if some methods consider the location, they only consider the physical location of the user which cannot be used for capturing the user's intention. We propose an effective method to calculate the user similarity using the semantics of the location. By using the semantics of the location, we can capture the user's intention and interest. Moreover, we can calculate the similarity between different locations using the hierarchical location category. To the best of our knowledge, this is the first research that uses the semantics of the location in order to calculate the user similarity. We evaluate the proposed method with a real-world use case: finding the most similar user of a user. We collected more than 251,000 visited locations over 591 users from foursquare. The experimental results show that the proposed method outperforms a popular existing method calculating the user similarity.

Online social network services have become a popular web activity to establish online social relationship among the people all around the world.These online social networking services allow its users to share opinions or posts on any high impact events in the world.The primary task of these services is to sort out credible posts and provide credible information about the event to the users. In this paper, the focus has been on Twitter, a rapidly growing micro blogging platform, which provides a large amount, diversity and varying quality of content. As Twitter is open to all, it emerged as an excellent means to disseminate information to a large user community in the shortest time. Due to very open, uncontrolled nature, Twitter has become vulnerable to incredible information from malicious and credulous users. Consequently, it is important to formulate sophisticated methods for analysis of credibility and relevance for ranking tweets. In this paper, tweets of an event posted by users have been collected and allowed to perform annotation process on those tweets by three human annotators to assess the tweet credibility. In order to provide ranks to the tweets according to the features, content based features are extracted. The performance of the ranking strategy used to rank tweets according content based features has been enhanced by the re-ranking strategy which uses context specific features such as event specific words to re-rank the tweets. After re-ranking tweets, an evaluation has been carried out by NDCG metric to measure the accuracy of re-ranking strategy.

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.

Today’s online social networking services (OSN) such as Facebook, Twitter are centralized. Users’ information may be leaked by these service providers, which results in potential privacy problems. Decentralized online social networking (DOSN) is provided to solve the problems. This paper presents a secure decentralized online social networking framework named SEDOSN. A peer-to-peer (p2p) online networking system is designed, and attribute-based encryption (ABE) technique is used. Different to existing ABE schemes which relies on a single authority or several authorities to authorize users and generating decryption keys for uses, we firstly proposed an ABE scheme with discretionary authorization. In our system, each user can authorize his friend, and generate decryption keys for his friend based on his friend’s attributes. We give our implementation and experiment results in the paper.

With Online Social Networking (OSN) services coming to prominence as a major platform for the popularization of Web content, recent research has focused on using information gleaned from these services to improve the performance of Content Distribution Networks (CDN). In this paper we find that contrary to popular belief, the simple cache replacement policy LRU outperforms methods based on OSN input. To establish this we propose an OSN-assisted caching strategy which we evaluate using a large Twitter dataset and through simulation. Our results show that vanilla LRU mostly outperforms the OSN-aided mechanisms. We study the reasons for this and find that if at any time there is a large enough percentage of highly popular content, which becomes popular within a short enough time span, CDN can do well by just following an LRU principle. While we do not claim generalizability to other workloads, or other caching architectures on the Internet, our results are important as they apply to the common use-case and show that simple algorithms do quite well.