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Sensitive Label Privacy Protection on Social Network Data

Sensitive Label Privacy Protection on Social Network Data

The algorithms are designed to provide privacy protection while losing as little information and while preserving as much utility as possible. In view of the tradeo between data privacy and utility [16], we evaluate empirically the extent to which the algorithms preserve the original graph's structure and properties such as density, degree distribution and clustering coefficient. We show that our solution is effective, efficient and scalable while offering stronger privacy guarantees than those in previous research, and that our algorithms scale well as data size grows. Privacy is one of the major concerns when publishing or sharing social network data for social science research and business analysis. Recently, researchers have developed privacy models similar to k- anonymity to prevent node reidentification through structure information. However, even when these privacy models are enforced, an attacker may still be able to infer one's private information if a group of nodes largely share the same sensitive labels (i.e., attributes). In other words, the label-node relationship is not well protected by pure structure anonymization methods. Furthermore, existing approaches, which rely on edge editing or node clustering, may significantly alter key graph properties. In this paper, we define a k-degree-l-diversity anonymity model that considers the protection of structural information as well as sensitive labels of individuals. We further propose a novel anonymization methodology based on adding noise nodes. We develop a new algorithm by adding noise nodes into the original graph with the consideration of introducing the least distortion to graph properties. Most importantly, we provide a rigorous analysis of the theoretical bounds on the number of noise nodes added and their impacts on an important graph property. We conduct extensive experiments to evaluate the effectiveness of the proposed technique.

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A Dynamic Social Network Data Publishing Algorithm Based on Differential Privacy

A Dynamic Social Network Data Publishing Algorithm Based on Differential Privacy

DOI: 10.4236/jis.2017.84021 329 Journal of Information Security network has great research value and application significance. The user’s large number of personal privacy information may be leaked when social network da- ta is analyzed and excavated. Social networks are evolving and changing that named dynamic social networks. The dynamic social network is concerned with the dynamic change caused by the change of time in the interaction between so- cial members. The privacy strategy of the static social network data release usually cannot adapt to the dynamic development of social network efficiently. It has far-reaching theoretical significance and practical value in the field of infor- mation security and network space security. Existing privacy protection tech- nologies include anonymous technology, data encryption technology, differen- tial privacy technology, privacy information retrieval technology, and accounta- bility system. The social network privacy protection method mainly studies the static social network data dissemination.

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SOCIAL NETWORK DATA RETRIEVAL USING SEMANTIC TECHNOLOGY

SOCIAL NETWORK DATA RETRIEVAL USING SEMANTIC TECHNOLOGY

Through semantic web, the information is stored in the semantic web in a structured way of information by that a computer can easily identify and analyze that how many users are connected with social network, so it can be implemented by SNS; it means that social network service provides all the services related to social networking sites such as Facebook and Twitter [2]. Hence, semantic web and social network service can combine with each other to build up the semantic social network service. Using this service, a computer system can be more helpful in activities such as locating the data in different formats. Semantic social network service can improve the efficiency of information sharing and integration. In this paper, the social network will be retrieved by performing the semantic search operation and by using an ontology which will integrate with each other, and one more thing is that in this research, the social network data will be analyzed with the help of Rtool which shows the relationship among all the people who are connected with the social network. The format of semantic web is in resource description framework (RDF) format, that is, RDF that contains the owl file.

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A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

regarding any individual organization, can be published online at any time, in which there is a risk of information leakage of anyone’s personal data. Hence, preserving the privacy of individual organizations and companies is needed before data are published online. Therefore, this research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exist for tabular data but we cannot apply directly to the structured complex graph data, which consist of vertices represented as individuals and edges represented as some kind of connection or relationship between the nodes. Various techniques such as K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes, and techniques such as edge perturbation and edge randomization are available to provide privacy to edges in social network graphs. Development of new techniques by integration into the exiting techniques such as K-anonymity, edge perturbation, edge randomization, and L-diversity to provide more privacy to relational data and social network data is ongoing in the best possible manner.

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Protecting Sensitive Labels in Social Network Data

Protecting Sensitive Labels in Social Network Data

easily. Unfortunately, most of the previous studies on privacy preservation data publishing can deal with relational data only, and cannot be applied to social network data. In this paper, we take an initiative towards preserving privacy in social network data. Specifically, we identify an essential type of privacy attacks: neighborhood attacks. If an adversary has some knowledge about the neighbors of a target victim and the relationship among the neighbors, the victim may be re- identified from a social network even if the victim’s identity is preserved using the conventional anonymization techniques. To protect privacy against neighborhood attacks, we extend the conventional k-anonymity and l-diversity models from relational data to social network data. We show that the problems of computing optimal k-anonymous and l- diverse social networks are NP-hard. We develop practical solutions to the problems. The empirical study indicates that the anonymized social network data by our methods can still be used to answer aggregate network queries with high accuracy. The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Although such analysis can facilitate better understanding of sociological, behavioral, and other interesting phenomena, there is growing concern about personal privacy being breached, thereby requiring effective anonymization techniques.

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Reliable online social network data collection

Reliable online social network data collection

Researchers have occasionally resorted to tricks to access data about users. For instance, a common way to access users’ Facebook profiles was to create accounts within the same regional network 3 than the target profiles. [45, 29, 43] Since mem- bership in regional networks was unauthenticated and open to all users, the majority of Facebook users belonged to at least one regional network. [45] And since most users do not modify their default privacy settings, a large portion of Facebook users’ profiles could be accessed by crawling regional networks. But this trick still did not allow to access all the profiles, as some privacy-sensitive users may have restricted access. Another trick is to log in to Facebook with an account belonging to the same university network as the studied sample. Lewis et al. [28] collect data on under- graduate students from Facebook by using an undergraduate Facebook account to access more data. Profiles can also be accessed by asking target users for friend- ship. Among 5,063 random target profiles, Nagle and Singh [31] were able to gain access to 19% of them after they accepted friend requests. They asked 3,549 of this set’s friends for friendship, and 55% of them accepted, providing them with access to even more profiles. But when studying privacy concerns, the set of profiles that have been accessed may be biased, as they belong to users who accept unknown friendship requests.

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Privacy to Social Network Data

Privacy to Social Network Data

To our knowledge, Zhou and Pei [25, 26] and Yuan et al. [23] were the first to consider modelling social networks as labelled graphs, similarly to what we consider in this paper. To prevent re- identification attacks by adversaries with immediate neighbourhood structural knowledge, Zhou and Pei [25] propose a method that groups nodes and anonymzes the neighbourhoods of nodes in the same group by generalizing node labels and adding edges. They enforce a k-anonymity privacy constraint on the graph, each node of which is guaranteed to have the same 4 Sensitive Label Privacy Protection on Social Network Data immediate neighbourhood structure with other k-1 nodes. In [26], they improve the privacy guarantee provided by k-anonymity with the idea of l-diversity, to protect labels on nodes as well. Yuan et al. [23] try to be more practical by considering users' different privacy concerns. They divide privacy requirements into three levels, and suggest methods to generalize labels and modify structure corresponding to every privacy demand. Nevertheless, neither Zhou and Pei, nor Yuan et al. consider labels as a part of the background knowledge. However, in case adversaries hold label information, the methods of [25, 26, 23] cannot achieve the same privacy guarantee. Moreover, as with the context of micro data, a graph that satisfies a k-anonymity privacy guarantee may still leak sensitive information regarding its labels [13].

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A Survey on Sentiment Analysis on Social Network Data

A Survey on Sentiment Analysis on Social Network Data

We have considered several methods for sentiment analysis by means of machine learning methods like Naïve Bayes, SVM etc. The studies have done the summarization of procedures, physical time incident discovery as well as sentence based sentiment classification exactly and proficiently. Naive Bayes classifier is unresponsive to unstable data which provide additional exact domino effect.

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Taxonomy of social network data types

Taxonomy of social network data types

Besides data types in OSNs and their classification, cur- rent research lacks an analysis of the privacy risks which single data types of the proposed taxonomies are accom- panied with. A first step toward this direction has been made by Liu and Terzi [30] who proposed a framework for computing the privacy scores of users on OSNs. They developed mathematical models to assess the sensitiv- ity and visibility of disclosed information. Their privacy score as an aggregation of combined sensitivity and vis- ibility values provides a measure for the privacy risk a user faces. As the data basis for this score only involves the user’s current privacy settings and the user’s posi- tion in the social network, the authors assume that the user alone is responsible for the dissemination of his privacy-relevant data. However, as we will point out dur- ing the development of our taxonomy, privacy is signifi- cantly impacted by the creator and the publisher of data as well as the domain information is published in. The fact that privacy relevant data can be disclosed outside the user’s domain bears additional potential risks. Fur- ther approaches to measure privacy risks include the work of Cutillo et al. [31] and Becker et al. [32]. The former

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User Expertise Modelling Using Social Network Data

User Expertise Modelling Using Social Network Data

In [Ho16], the authors assumed that experts on Twitter will behave differently from non- expert users. They intended to identify the discriminative behaviours of expert users and hoped to use them for expertise location on Twitter. To achieve this goal, three groups of features from the tweets or retweets posted by users were defined: behavioral features (e.g. the number of followers and the number of hashtags per tweet), linguistic features (e.g. the user’s lexical coherence and frequency of least common word used in the tweets) and stylistic features (e.g. the number of question marks used and the number of sentiment terms used). Then they harvested expert users on four specific topics, Science, Technology, Health & Fitness and Business, by manually selecting related webpages in which expert Twitter users are recommended on certain topics. Finally, logistic regression was employed to distinguish expert users on the four topics by using the three different groups of features. Through analyzing the experimental results, a number of important findings on expertise discovery in the Twitter network were summarized at the end of the work, such as, experts tend to be older Twitter users than other users, experts use significantly more specialized language, and experts tend to retweet posts with more complex sentences. However, the authors did not convert those findings into effective expertise identification approaches and only gave the conclusions based on some statistical results. Their usefulness in the construction of advanced approaches and application in other topics remains unclear.

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Social Network Data Analysis for User Stress Discovery and Recovery

Social Network Data Analysis for User Stress Discovery and Recovery

The social networks are build to exchange images, videos and reviews between the members. The members can connect from anywhere and anytime. The checkin locations and time information are maintained under the Location Based Social Networks (LBSN). Location-Based Social Networks (LBSNs) provides the facility to access people’s locations, profiles and online social connections. High scalable, high volume and high velocity data items are processed using big data mining models. The social network data values are analyzed to estimate the user behavior and stress levels. The correlations of user interactions and stress are analyzed for the stress detection process. The social network data values are parsed and three types of attributes are extracted for the stress detection process. Stress-related textual, visual and social attributes are extracted for the stress detection process. The hybrid model combines the Factor Graph and the Convolutional Neural Network (CNN) for stress detection. Tweet content and social interaction are analyzed in the hybrid model.

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Privacy Model for Anonymizing Sensitive Data in
          Social Network

Privacy Model for Anonymizing Sensitive Data in Social Network

Users find their friends and create the link between them with a distance in the network .As depicted in fig.7,a graph is constructed with the registered users using java universal network graph.The graph contains the individual’s id,label and degree information. From that graph ,adversary can infer peoples and try to get the sensitive labels and informations of the individuals in the graph. When publishing social network data, graph structures are also published with corresponding social relationships. As a result, it may be exploited as a new means to compromise privacy. A structure attack refers to an attack that uses the structure information, such as the degree and the subgraph of a node, to identify the node.

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A Survey of Data Mining Techniques for Social Network Analysis

A Survey of Data Mining Techniques for Social Network Analysis

Social networks are important sources of online interactions and contents sharing [81], [21], subjectivity [6], assessments [52], approaches [54], evaluation [48], influences [8], observations [24], feelings [46], opinions and sentiments expressions [66] borne out in text, reviews, blogs, discussions, news, remarks, reactions, or some other documents [57]. Before the advent of social network, the homepages was popularly used in the late 1990s which made it possible for average internet users to share information. However, the activities on social network in recent times seem to have transformed the World Wide Web (www) into its intended original creation. Social network platforms enable rapid information exchange between users regardless of the location. Many organisations, individuals and even government of countries now follow the activities on social network. The network enables big organisations, celebrities, government official and government bodies to obtain knowledge on how their audience reacts to postings that concerns them out of the enormous data generated on social network (as shown in Fig. 2). The network permits the effective collection of large-scale data which gives rise to major computational challenges. However, the application of efficient data mining techniques has made it possible for users to discover valuable, accurate and useful knowledge from social network data.

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The Role of Dialogic Teaching in Fostering Critical Literacy in an Urban High School English Classroom

The Role of Dialogic Teaching in Fostering Critical Literacy in an Urban High School English Classroom

Consider a social network application that collects user data from social media plat- forms, for performing business analysis or providing services. Due to privacy concern, the users do not want to release their sensitive data to third-party social applications, so that sensitive data is generally sanitized prior to releasing. However, it is possible to mine sensi- tive information carried in collected data by data mining techniques, to contribute to more commercial benefit. We proposed that well-designed data sanitization can be developed for realizing privacy-utility tradeoff in social network data publishing, although powerful attack- ers are presented with a broad range of auxiliary information to launch inference attacks. Such sanitization helps users sanitize their data by deleting some attributes, inserting other attributes, and perturbing some attributes, thereby hiding private information within ran- domness. Meanwhile, such sanitization should enable applications effectively recover useful information from sanitized data for data utility concern. Searching a well-designed saniti- zation method is highly non-trivial as data utility is restricted in sanitization process. Our works show that this issue can be alleviated by identifying the implicit dependency relation- ship encoded in data, and incorporating social attribute sanitization and link sanitization simultaneously, etc.

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Title: Sanitization Techniques against Personal Information Inference Attack on Social Network

Title: Sanitization Techniques against Personal Information Inference Attack on Social Network

Desired use of data and individual privacy presents an opportunity for privacy preserving social network. That is the discovery of information and relationships from social network data without violating privacy .But the problem of sanitizing a social network to prevent inference of social network data and then examines the effectiveness of those approaches on a user profile data set. In order to protect privacy, sanitize both details and the underlying link structure of the graph. That is delete some information from a users profile and remove some links between friends. In many situations the data needs to be published and shared with others. Social networks are online applications that allow their users to connect by means of various linktypes. As part of their professional network because of users specify details which are related to their professional life. These sites gather extensive personal information, social network application providers have a rare opportunity direct use of this information could be useful to advertisers for direct marketing. In such situation need to prevent inference attack by using sanitization technique ,sanitize the data set before release to third party ,it is very helpful to user for publishing their details on social site.

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Data Preservation And Breach Detection On Social Network

Data Preservation And Breach Detection On Social Network

Fig. 5 illustrates the data downloaded from the web and how it was uploaded on Gephi tool in form .gdf format. After uploading the social network data, the tool analyses the data and represents it in form of graph with nodes and edges in Fig. 6. Each cluster of nodes with distinguished color represents users that belong to a particular community or society. Fig. 7a depicts the lattice graph representation of the social network data after it has been anonymized with KDLD method in addition with Re- identification risk model with the ARX anonymization tool, from that result it can be seen that there was minimal information loss. In Fig. 7b the data that was anonymized was then further analyzed with the Gephi tool, from the graph representation, it can be seen that each user cannot be identified or be linked to a particular society because they are all interwoven. Fig 8 shows that the re-identification risk of each record in the social network data after anonymization reduced, the re- identification risks were very high before anonymization, this can be seen on the left-hand partition of Fig. 8 while the right-hand partitions depicts the re-identification risk after anonymization. The exported and published data are shown in Fig 9 and Fig. 10 respectively.

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Taxonomy for Privacy Policies of Social Networks Sites

Taxonomy for Privacy Policies of Social Networks Sites

Differently from what happens in Information Collec- tion, each pair {information processing, data} has a given value associated with and may be House (H), Third Party (TP), Data Provider (DP), Users (U) or Not Allowed (N). More than one value at a given par is allowed. House is the social networking site, Third Party are third parties who may be advertisers on the site, developers of social applications for the site, among others. Data provider is the user who is providing the data and Users are other users of the network. By assigning one of the values cited above for the specific pair, we are actually classifying who is responsible for the privacy action for an item of data, if applicable. For example, if a social networking site has value H for the pair {Aggregation, Disclosed} this means that the site can aggregate user Disclosed data and if this happen does not means privacy invasion be- cause the user is aware of such action. Figures 6 and 7 illustrate, respectively, Information Dissemination and Invasion parts and are similar to the interpretation of In- formation Processing.

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Social Network Analysis using Data Segmentation and Neural Networks

Social Network Analysis using Data Segmentation and Neural Networks

Abstract - Social Network analysis is one of the most important data analysis methods, where we gather data from the social media and network of a customer. With a lot of research and using different data segmentation and machine learning techniques we gather a deep insight of the kind of profile/category the customer belong to. Furthermore, we explore the possibility of leveraging this model and applying it in various other entities by using K-Means clustering and Natural language Processing into a multi class regression model. Further, we also interpret the model by analyzing the various attributes and use mathematical methods of Graph theory and information gain, to fit the model into an Artificial Neural network(ANN) and produce results as described in the paper.

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WELL-RGANIZED COMMUNICATION SYSTEM BETWEEN DATA ANDSOCIAL NETWORKS

WELL-RGANIZED COMMUNICATION SYSTEM BETWEEN DATA ANDSOCIAL NETWORKS

We propose a novel collaborative face identification frame work, humanizing the accuracy of face annotation by effectively making use of multiple face recognition engines available in online social networks. Our collaborative face recognition framework consists of two major parts: merging or union and selection of face recognition engines of multiple face recognition results.

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Data Replication in Online Social Network on Datacenters

Data Replication in Online Social Network on Datacenters

While a new OSN model with many small, globally distributed data centers will result in improved service latencies for users, a critical challenge in enabling such a model is reducing inter-datacenter communications (i.e., network load). Thus, we propose the Selective Data replication mechanism in Distributed Datacenters (SD3) to reduce interdata center communications while achieving low service latency. We verify the advantages of the new OSN model and present OSN properties from the analysis of our trace datasets to show the design rationale of SD3. Some friends may not have frequent interactions and some distant friends may have frequent interactions. In SD3, rather than relying on static friendship, each datacenter refers to the real user interactions and jointly considers the update load and save dvisit load in determining replication in order to reduce interdatacenter communications. Also, since different atomized data has different update rates, each datacenter only replicates atomized data that saves inter-datacenter communications, rather than replicating a user’s entire dataset. SD3also has a locality-aware multicast update tree for consistency maintenance

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