Towards an Open and Heterogeneous Social Web

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.

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.

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.

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’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.

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 networks—Paradise of computer viruses

In the world of e-marketing, new business models are introduced to accommodate changes caused by various factors, including the markets, the services, the customers, among others. One latest trend of e-marketing is social networking Web sites, many of which have attracted not only large number of users and visitors, but also business companies to place their online ads on the sites. As an important example of Web 2.0 applications, online social networks deserve comprehensive studying and analysis; they are not only employed as an effective vehicle of e-marketing, but may impact how future Web-based applications would be developed. In this chapter, we explore online social networking as a new trend of e-marketing, by conducting a comparative analysis of online social networking sites. We first discuss the various types of online social networks, based on the classification by Laudon & Traver (2008), and then analyze online social networks from a business strategy point of view, by discussing the primary revenue models for online social networking sites. The primary contribution of this chapter is a comparative analysis and discussions of representative online social networking sites and their respective revenue model(s). This chapter aims to provide the reader with a basic understanding of the emerging online social networking Web sites and their primary revenue models.

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 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 Networks (OSNs) represent an important source of information since they manage a huge amount of data that can be used in many different contexts. Moreover, many people create and manage more than one social profile in the different available OSNs. The combination and the extraction of the set of data from contained in OSNs can produce a huge amount of additional information regarding both a single person and the overall society. Consequently, the data extraction from multiple social networks is a topic of growing interest. There are many techniques and technologies for data extraction from a single OSN, but there is a lack of simple query languages which can be used by programmers to retrieve data, correlate resources and integrate results from multiple OSNs. This work describes a novel query language for data extraction from multiple OSNs and the related supporting tool to edit and validate queries. With respect to existing languages, the designed language is general enough to include the variety of resources managed by the different OSNs. Moreover, thanks to the support of the editing environment, the language syntax can be customised by programmers to express searching criteria that are specific for a social network.

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.

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.

Recent developments in Online Social Network OSN technologies and services, added with availability of wide range of applications has paved the way towards popularity of several social network platforms. These OSNs have evolved as a major communication and interaction platform for millions of users worldwide. The users interact with their social contacts by using various types of available services like messaging, sharing pictures /videos, and many more. However, a major drawback of these platforms is that these activities might reveal certain private information about the users unintentionally. Whenever a user shares any information on OSN with his friends, the information is prone to leakage to other users. The probability of leakage increases with the visibility of the user himself i.e. the number of users who would be interested on the information of the user as well as the visibility of his/her friends. Therefore, it is important to measure the visibility of a user in the OSN community. This paper proposes a measure for the visibility of a user, by considering the connectivity properties of the users present in the network. The characteristics of the proposed measure is studied on a real Twitter network as well as a generated Erdi?źs-Renyi network, where we observe the relation between visibility and certain topological parameters of the network. The results show that visibility of a user is determined by his/her direct social contacts, i.e. the number of followers in case of Twitter. However, evaluating the visibility of an user is practically difficult considering the immensely large size of the OSN's. These findings help us to generate simple mechanisms to estimate the visibility of a user using only its local connectivity properties.

Recently, online social networks such as Twitter and Facebook have become one of the most important information communication platforms, which contain large number of useful user data for various research fields. Extracting information from online social network pages is the fundamental key technique of online social network study. In this paper, we have proposed a general web page extraction method according to the structural feature of online social network web pages. Based on our method we can select useful information in web page with class tag frequency and after some simple string manipulation processes the exact content will be achieved. Furthermore, we have proposed an automatic particular regular expression generation algorithm which can make our content extraction method apply to many similar structure online social networks without re-writing the entire extraction process facing to each web site. The experimental results show that our approach has good adaptability and shows significant advantages in both precision and recall.