FACTORS TO CALCULATE FRIENDSHIP INTENSITY

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Privacy and security of OSNs

Apart from the benefits, OSNs give rise to privacy and security threats over the Internet, more severely than before. Lots of studies are conducted to understand peculiar human nature about the privacy of their data. Discussions about contradictory privacy preferences among human and its importance with respect to modern information age can be found in [24] and [25]. Boyd [25] highlights human aspects related to “privacy” and “publicity”. According to her individuals publicize their data cautiously to gain some benefits or instant fame.
The research conducted by Gross et al [26] and Krishnamurthy [27], are two influential studies in OSN privacy and security. These authors highlight many privacy issues of OSN sites. Gross in his study draw attention to the factors that compel users to reveal their most private data. Krishnamurthy has the credit of characterizing different aspects of privacy. He has conducted a study of many OSN sites in order to analyze the privacy controls provided by these OSNs. Furthermore, he also argues to divide user data into small chunks for providing more efficient access control. Additionally, he further suggests that OSNs should only provide required data to the third party application and games. Finally, in other studies, the researchers cover security and privacy threats [28, 29] to the social network data at different levels [3] and their solutions [30-32].

Calculating friendship intensity through data mining

We did not find any research which applies data mining algorithms to calculate friendship strength in OSNs. Therefore, this research is the first stride in this direction.
As far as calculation of friendship intensity is concerned, we found an interesting ongoing study, that is conducted by Banks et al. [4]. The authors have introduced interaction count method in which they take different types of interactions and count them in order to calculate friendship strength. In addition to provide a novel intensity calculation method, they also suggest a framework that utilizes calculated friendship intensity for better privacy control in OSNs.

Social Network Analysis

The evolution, interaction patterns and growth of social network contain a lot of interesting realities that can be examined by performing a systematic study of social networks. This systematic study is termed as Social Network Analysis (SNA). Although, SNA is performed from last 50 years in social science such as Sociology, Social Psychology and Anthropology but with the emergence of OSNs, this research has been revived, considerably. On one hand, OSNs provide a laboratory to experiment many of social theories such as Small World Phenomena, Weak Ties. On the other hand, OSNs are also revealed several social aspects regarding individuals, groups, organizations and nations which were never observed before. The SNA analyst tries to cover the network of relations as fully as possible to analyze the flow of information, and to observe what effects these relations leave on individuals and organizations. According to Laura et al. [16], SNA is performed in following phases:
• Sample Selection.
• Data Collection.
• Data Analysis using SNA method.
• Conclusions.
In SNA, first of all the target group selection is performed to identify the patterns that exist in that particular network. This selected group of nodes and connections is called sample or population [16, 18]. Many methods are used to collect information from that sample. These methods include questionnaires, interviews, observations, diaries and through computer monitoring [16]. In OSNs, crawling and monitoring methods are quite common to observe the overall structure of network. To crawl the OSN, an artificial agent or simple software move from one node to the other (from friend to friend) systematically (commonly used method are Breadth First Search (BFS), Depth First Search (DFS) and their variants). The movement of this software agent is then simulated to observe the overall structure of network. In addition to this, recording the patterns of interactions between individuals over a longer period of time, is another commonly used method in computer based social networks. The type of the data which is collected from the sample is also called units of analysis which consists of relations, ties and actors [16].
After the collection of data, the next step is to analyze this data by using methods such as full network , snowball and ego-centric methods [16]. The Full network method provides whole picture of the network but it becomes complex when there are many actors in the network and each possible connection between actors need to be considered [16]. Moreover, a complete list of connections between people as well as their links to external environment is created while performing full network analysis which makes this process quite resource consuming [15, 16]. The ego-centric method is quite useful when complete network analysis is not required [15]. In this technique, first the “ego”, starting point of analysis, is identified and then the process is moved to his “alters”; connections that are one step away or friends in most OSNs [15, 18]. This technique has a variant which is called “ego only”, where analyst only consider first level connections [18]. The Figure 2.5(b) depicts the egocentric social network where red node represents the ego and remaining nodes are alters. If the identified alters in an egocentric network becomes egos to continue the analysis, then this method is referred as Snowball method [18]. Snowball method is also time and resource consuming and one recently developed method which covers the limitation of Snowball method, is referred as Hybrid method [35]. Hybrid method only tries to analyze important alters despite considering all alters, and these alters are nominated by the egos. Finally, conclusion is the last step in SNS where analyst tries to come up with new findings or just prove/disprove some hypothesis.

Online Social Network Analysis

A lot of work is carried out in recent years to analyze patterns and growth of OSNs in a manner that how they affect or affected by real life social network. Researchers observe intriguing similarities between OSNs and traditional real life social networks [20]. Barabasi proved that OSN has power-law, scale-free growth and exhibit preferential attachment [20]. More influential research on OSNs is conducted by Mislove et al. [36] and Ravi et al. [22, 23]. Mislove et al. performed a large study of four OSNs; Flickr, YouTube, LiveJournal, and Orkut. They collected the data of 11.3 million users and 328 million links by crawling publically accessible profiles. They proved many of social theories i.e. power-law, small-world, and scalefree properties of OSNs from their findings. They also examined that these networks contain densely connected core of high degree nodes which is connected by small group of low-degree nodes.
OSNs can be viewed as a graph, G= (V, E) where V shows set of nodes i.e. v1, v2 …vn and E represents set of edges that connect these nodes. Two nodes are connected in the graph, if an edge exists between these nodes. Furthermore, if we can reach from a node vi to vj by passing through one or more intermediate nodes then the path between vi and vj exists in that particular graph. The path between two node in a graph is denoted as vivj which represents sequence of nodes that should be traversed to reach vj. A graph is strongly connected if for any two nodes there exist ab and ba. Moreover, a graph can be sliced into one or more sub-graphs of strongly connected components (SCC) [37]. In SNA, SCCs are used to identify strongly connected sub-groups in the social graph. Some important metrics that are used in OSN analysis are described below:
Size: The size of G is denoted as n = |V|, it represents number of people in the social network. Density: Density of the graph or SCC shows the ratio between the individuals and their relations (connections). The minimum density of a graph is 1/n (it is the case when graph is a ring) and maximum density is 1.
Diameter: Diameter of a graph shows maximum length between any two nodes. The diameter of a social graph is between 1 and n.
Adjacent matrix: Adjacent matrix is just a matrix representation of the graph or SCC. This is a matrix of size n × n where ai, j=1, if vvj exists and 0 otherwise.

Social network sites

Social network sites or OSN sites are type of OSNs which have revolutionized the Internet. Unlike other websites where documents are linked with other document, in OSN sites people are linked with other people to form computerized social network. OSN sites has magnetized numerous Internet users in just last five years which also open a window of new research opportunities in many areas such as Sociology, Anthropology, and Computer Science etc. There are hundreds of OSN sites with different technological capabilities, supporting a wide range of interests and practices [1]. Many of OSN sites started with the concept of “social networking” means; people will develop new online acquaintances to expand their social network. But this is mostly not the case with OSN sites where individuals only like to automate their “latent ties”; the people to whom they share real life connections [1]. Therefore, people only like to share among their real life network in the most of large OSN sites.
OSN sites did not observe much excitement in the beginning when first website (sixdegrees.com) of this type was launched in 1997 which only survived for three years and its founder thought that “its ahead of time” [1, 2, 13] .The peak time of OSN sites‟ emergence and popularity, was from 2002 to 2004 when some of famous OSN sites i.e. Friendster, MySpace, Bebo, LinkedIn and Facebook were launched. OSN sites has started to flourish from 2005 onward and at present, OSN sites are among the top Internet websites in terms of user base and Internet traffic [2]. Table 2.2 presents top five OSN sites, their user base and website rank.
OSN sites are classified in terms of their use, features and purpose. One classification is internal social network (ISN) and external social network (ESN) [13]. In this division, the former type of social networks comprises closed/private networks within a society, business or organization while the later type is open/public social network which is opened for everyone to create and evolve their interest communities. Most of the large OSN websites are instances of ESN i.e. Facebook, MySpace etc. Besides this categorization, social network websites can also be divided according to their purpose or some particular interest. In this regard, OSN sites are divided into two broad classes: dedicated social network (DSNS) and multipurpose social network websites (MSNS). In DSNS, social networks are developed to perform some specific pursuit or task i.e. dating, picture sharing, video sharing. Livejournal5, YouTube6 and Date.com7 are examples of DSNS. On the other hand, multipurpose OSN sites allow performing almost any activity according to one‟s own interest. MSNS instances include Facebook, MySpace, and Twitter etc.

Features of OSN sites

Besides a variety of exclusive features, OSN sites also increased the utilization of numerous already available Internet resources and applications. OSN sites provide various features which range from socializing with friends to sharing or recommending external web pages or resources (i.e. hyperlinks, videos, news etc.). These features are more or less same in the top social utility providers. Figure 2.5 depicts the process of using OSN sites from initiation to its continuous utilization.
Like most websites, user registration is the first step in an OSN site. After registration, user is asked to create his profile which comprises various type of information i.e. picture, contact, education, address, interests etc. The user profile consists of user‟s personal information which could be alluring for potential privacy attacks. In these circumstances, the profile visibility is an important issue which depends on the site‟s privacy policy and user discretion [1]. Some of the websites allow external users (individual who are not even part of OSN site) and applications (crawlers) to view or extract information from the user profile in their default settings. In Facebook, profile visibility varies at different levels such as friend, friend of friend or external user. Friends can view the profiles of their friends but profile visibility for other levels depends on the user‟s own choice.
After creating his public/private persona, user can create relationships with other members of that particular website. This relationship is mostly labeled as friendship, fan or contact [1]. Friendship is most commonly used relationship in almost all OSN sites which may not depict exact description of the relationship that some of individuals may be bonded in reality. Later on, individuals search or invite their friends to form a user-centered social network. In most of the websites, the friendship relationship is bi-directional which means friendship confirmation is required from both sides. For example, if X sends friendship request to Y then Y‟s confirmation/acceptance is required to be a part of X‟s social network. Some websites also allow unidirectional relationship in form of fan or follower [1]. While dealing with user privacy, the visibility of social network (Friend‟s list) is also a crucial aspect for OSN sites. The majority of the websites permit everyone to view or traverse the friend‟s list of a particular user, but some also facilitate their users to control the visibility of their friend‟s list. Moreover, traversing someone‟s friend‟s list (or social graph) is most basic activity in SNA.

Table of contents :

1 INTRODUCTION
1.1 THE RESEARCH DOMAIN
1.2 AIMS AND OBJECTIVES
1.3 RESEARCH QUESTIONS
1.4 RESEARCH METHODOLOGY
1.4.1 Case study
1.4.2 Online survey
1.4.3 Experimentation
1.5 VALIDITY THREATS
1.6 RELATED WORK
1.6.1 Online Social Networks
1.6.2 Privacy and security of OSNs
1.6.3 Calculating friendship intensity through data mining
1.7 CONTRIBUTIONS
1.7.1 OSN and privacy
1.7.2 Friendship levels prediction
1.8 THESIS OUTLINE
2 ONLINE SOCIAL NETWORKS AND PRIVACY
2.1 INTRODUCTION
2.2 ONLINE SOCIAL NETWORKS
2.2.1 Social Network Analysis
2.3 SOCIAL NETWORK SITES
2.3.1 Features of OSN sites
2.4 OSN AND PRIVACY
2.4.1 Information revelation and user control
2.4.2 Who wants my private data?
2.5 PRIVACY RISKS IN OSNS
2.5.1 Privacy lapses at the social network level
2.5.2 Privacy threats at the application service level
2.6 PRESERVING USER PRIVACY IN OSN
2.6.1 Technical Methods
2.6.2 Market regulations and Government rules
3 USER PRIVACY CONCERNS
3.1 INTRODUCTION
3.2 PRIVACY CONCERNS SURVEY
3.2.1 Methods
3.2.2 Survey results and discussions
3.2.3 Privacy Concerns
3.2.4 Privacy preserving habits
3.2.5 Privacy threats
3.2.6 Concerns over governmental interference
3.2.7 Facebook privacy settings
3.3 SUMMARY OF SURVEY RESULTS
4 FRIENDSHIP INTENSITY CALCULATION
4.1 INTRODUCTION
4.2 FACTORS TO CALCULATE FRIENDSHIP INTENSITY
4.2.1 OSN Interactions
4.2.2 Mutual friends
4.2.3 Profile visits
4.3 FRIENDSHIP INTENSITY USING DATA MINING
4.4 THE EXPERIMENTAL PROCEDURE
4.4.1 Data set
4.4.2 Algorithms
4.4.3 Evaluation
4.5 FRAMEWORK FOR UTILIZING FRIENDSHIP LEVELS
5 CONCLUSIONS AND FUTURE WORK
5.1 CONCLUSIONS
5.2 FUTURE WORK
6 REFERENCES 

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