Ontology-based access control for social network systems

As the information flowing around in social network systems is mainly related or can be attributed to their users, controlling access to such information by individual users becomes a crucial requirement. The intricate semantic relations among data objects, different users, and between data objects and users further add to the complexity of access control needs. In this paper, we propose an access control model based on semantic web technologies that takes into account the above mentioned complex relations. The proposed model enables expressing much more fine-grained access control policies on a social network knowledge base than the existing models. We demonstrate the applicability of our approach by implementing a proof-of-concept prototype of the proposed access control framework and evaluating its performance

Privacy Considerations when Designing Social Network Systems to Support Successful Ageing

A number of interventions exist to support older adults in ageing well and these typically involve support for an active and sociable ageing process. We set out to examine the privacy implications of an intervention that would monitor mobility and share lifestyle and health data with a community of trusted others. We took a privacy-by-design approach to the system in the early stages of its development, working with older adults to firstly understand their networks of trust and secondly understand their privacy concerns should information be exchanged across that network. We used a Johari Windows framework in the thematic analysis of our data, concluding that the social sharing of information in later life carried significant risk. Our participants worried about the social signaling associated with data sharing and were cautious about a system that had the potential to disrupt established networks

An Analysis Grid for Privacy-related Properties of Social Network Systems

International audienceSocial Network Systems (SNSs) are the predominant kind of web service around the world. They attract many users seeking popularity, entertainment and network building, along with ease of use. Most current SNSs are based on centralized designs, which are less likely to improve privacy since there is a single and central authority with exclusive administration control over user information. Many proposals have been introduced that work towards decentralizing the infrastructure support in order to enhance privacy in SNSs. However, designing decentralized social network systems (DSNS) driven by privacy is a hard task because privacy is impacted by most design choices. This paper proposes a multicriteria analysis grid designed to evaluate several properties of SNSs related to privacy trade-offs. Based on the analysis grid result, this paper also presents the application of lattice-based tools to classify and visualize social network systems in privacy-related hierarchies

A Dynamic Trust Relations-Based Friend Recommendation Algorithm in Social Network Systems

A discovered algorithm based on the dynamic trust relations of users in a social network system (SNS) was proposed aiming at getting useful information more efficiently in an SNS. The proposed dynamic model combined the interests and trust relations of users to explore their good friends for recommendations. First, the network based on the interests and trust relations of users was set up. Second, the temporal factor was added to the model, then a dynamic model of the degree of the interest and trust relations of the users was calculated. Lastly, the similarities among the users were measured via this dynamic model, and the recommendation list of good friends was achieved. Results showed that the proposed algorithm effectively described the changes in the interest similarities and trust relations of users with time, and the recommended result was more accurate and effective than the traditional ones

On-line privacy behavior: using user interfaces for salient factors

The problem of privacy in social networks is well documented within literature; users have privacy concerns however, they consistently disclose their sensitive information and leave it open to unintended third parties. While numerous causes of poor behaviour have been suggested by research the role of the User Interface (UI) and the system itself is underexplored. The field of Persuasive Technology would suggest that Social Network Systems persuade users to deviate from their normal or habitual behaviour. This paper makes the case that the UI can be used as the basis for user empowerment by informing them of their privacy at the point of interaction and reminding them of their privacy needs. The Theory of Planned Behaviour is introduced as a potential theoretical foundation for exploring the psychology behind privacy behaviour as it describes the salient factors that influence intention and action. Based on these factors of personal attitude, subjective norms and perceived control, a series of UIs are presented and implemented in controlled experiments examining their effect on personal information disclosure. This is combined with observations and interviews with the participants. Results from this initial, pilot experiment suggest groups with privacy salient information embedded exhibit less disclosure than the control group. This work reviews this approach as a method for exploring privacy behaviour and proposes further work required

Design and analysis of social network systems (SNS)

In the last few years, online Social Network Systems (SNSs) thrived and changed the overall outlook of the Internet. These systems play an important role in making the Internet social, a hallmark of Web 2.0. Various such systems have been developed to serve a diverse set of needs. SNSs provide not only a space for self-representation, but also mechanisms to build and maintain one’s social network online. A lot of studies have been carried out on such systems to identify how people develop cultures of communication, sharing and participation and also to identify the network structure of such systems. In this thesis, we carry this line of research forward. Our aim is the identification of some key user characteristics and social processes which result in the emergence of a social network. These might help future platform and application developers in creating better, more efficient and more open and user-friendly SNSs. Specifically, we make the following three major contributions: a) One of the distinct features of an SNS is the public listing of friendship links - social network. Most of the personal details such as hometown and workplace information have been hidden from non-friends, but the list of friendships remains open. Being a true representation, people use their real names as their screen names. Such names alone contain detailed cultural information about their ethnicities, religion and even their geographical origins. Our first contribution is that we have made good use of such information by inferring ethnic classification of users of Facebook. We identified how clustered and segregated the overall social network is when users’ inferred ethnicity is taken into account. Different cultures have different behaviours with distinct characteristics. This rich information can be used to develop an understanding and help create diverse applications catering for specific ethnicities and geographical regions; covering both the dominant and non-dominant groups. We have identified ethnicities of a subset of Facebook users with their friends and studied how different ethnicities are connected among and within each other. A large social network dataset of four thousand Manchester Metropolitan University (MMU) students have been selected from Facebook. We have extensively analysed this dataset for its network structure and also its semantic and social structure. Our work suggests our dataset is clustered and segregated on ethnic lines. b) To develop a user liberating SNS where the control and the ownership of rich personal data is in the hands of SNS users, a clear understanding is required of how such systems on an individual and group level are developed and maintained. Never before in Social Sciences was it possible to study society on such a large scale. These systems have facilitated the study of individuals both at a local and global scale. However, at the moment very little knowledge is available to identify how people develop their friendship in reality. So for example, it is not known whether in SNSs people meet others based on their attributes and interests, or if they simply bring online their real lives’ social networks. And more specifically, what processes does one go through to develop her social network. To fill this knowledge gap in this thesis, as our second contribution, we have used a computer simulation technique known as Agent-Based simulation, to develop four simulation models based on both individuals’ affinities and environmental aspects. Specifically, we have developed models of student interaction to develop social networks. Three University’s datasets which include Caltech (Nodes 762, Edges 16651), Princeton (Nodes 6575, Edges 293307) and Georgetown (Nodes 9388, Edges 425619), have been used to check the performance and rigour of the model. Our evidence suggests that ‘friend-of-a-friend’ (FOAF) best represents social interactions in Caltech University. In the case of Princeton and Georgetown, we found a multitude of social and structural processes involved, which are: attribute based (same dormitory, major or high school etc.), social interaction, random meet ups (through parties or other social events) and current friends introducing new friends. c) We observe that in the main, SNSs are centralised, and depend solely on central entities for everything. With huge personal data on such SNSs, advertising and marketing agencies have made very sophisticated systems to gather information about people. It is a goldmine for them for personalised advertisement. Also various governmental agencies have been using SNSs as an excuse to curb potential threats both legally and illegally, to obtain information on numerous users (people). In order to deal with such issues inherent in centralised client-server architecture, as the third contribution of this thesis, we have proposed and implemented a completely decentralised SNS in a peer-to-peer fashion. Our implementation is done in an open source Peer-To-Peer (P2P) client Tribler. To handle the dynamicity of users in a P2P system – their availability, we have developed mechanisms to deal with it. This SNS has been evaluated on a deployed system with real users. This prototype establishes the feasibility of a totally distributed SNS, but its practicality when scaled to a full system would require more work

On social networks and collaborative recommendation

Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data. We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks. In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.</p

Emergence, Evolution and Scaling of Online Social Networks

This work was partially supported by AFOSR under Grant No. FA9550-10-1-0083, NSF under Grant No. CDI-1026710, NSF of China under Grants Nos. 61473060 and 11275003, and NBRPC under Grant No. 2010CB731403. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD