Privacy Threat Analysis of Social Network Data

Social networks contain sensitive information, and direct press conferences disclose people’s privacy. The published social network data needs privacy protection. The currently released social network data has seriously damaged the network structure due to privacy protection. This paper proposes a social network graph publishing model that preserves the social network structure. This model builds an uncertainty graph based on privacy protection probability to generate social network data for publication. It is proved theoretically that the social network graph publishing model satisfies the definition of differential privacy. This paper uses three real social network datasets to evaluate the effectiveness of the proposed method, showing that the method effectively retains the network structure.

2. INTRODUCTION Since the inception of online social networks, the amount of social data that is being published on a daily basis has been increasing at an unprecedented rate. Smart, GPS-enabled, always-connected personal devices have taken the data generation to a new level by making it tremendously easy to generate and share social content like check-in information, likes, microblogs (e.g., Twitter), multimedia data, and so on. There is an enormous value in reasoning about such streaming data and deriving meaningful insights from it in real-time. Examples of potential applications include advertising, sentiment analysis, detecting natural disasters, social recommendations, personalized trends, spam detection, to name a few. There is thus an increasing need to build scalable systems to support such applications. Complex nature of social networks and their rapid evolution, coupled with the huge volume of streaming social data and the need for real-time processing, raise many computational challenges that have not been addressed in prior work. Social network data comprises of two major components. First, there is a network (linkage) component that captures the underlying interconnection structure among the entities in the social network. Second, there is content data that is typically associated with the nodes and the edges in the social network. The social network data stream contains updates to both these components. The structure of the network may itself change rapidly in many cases, especially when things like webpages and user tags (e.g., Twitter hashtags) are treated as nodes of the network. However, most of the social network data stream consists of updates to the data associated with the nodes and the edges, e.g., status updates and other content uploaded by the users, communication among the users, and so on. There is interest in performing a wide variety of queries and analytics over such data streams in real time. The queries can range from simple publish-subscribe queries, where a user is interested in being notified when something happens in his friend circle, to complex anomaly detection queries, where the goal is to identify anomalous behavior as early as possible. In this paper, we present an introduction to this new research area of stream querying and reasoning over social data. This area combines aspects from several well-studied research areas, chief among them, social network analysis, graph databases, and data streams. We provide a formal definition of the problem, survey the related prior work, and discuss some of the key research challenges that need to be addressed (and some of the solutions that have been proposed). We note that we use the term stream reasoning in this paper to encompass a broad range of tasks including various types of analytics, probabilistic reasoning, statistical inference, and logical reasoning. We contrast our use of this term with the recent work by Valle et al. [104, 105] who define this term more specifically to refer to integration of logical reasoning systems with data streams in the context of the Semantic Web. Given the vast amount of work on this and related topics, it is not our intention to be comprehensive in this brief overview. Rather we aim to cover some of the key ideas and representative work.

Every day, vast amounts of social networking data is being produced and consumed at a constantly increasing rate. A user’s digital footprint coming from social networks or mobile devices, such as comments, check-ins and GPS traces contains valuable information about her behavior under normal as well as emergency conditions. The collection and analysis of mobile and social networking data before, during and after a disaster opens new perspectives in areas such as real-time event detection, crisis management and personalization and provides valuable insights about the extent of the disaster, its impact on the affected population and the rate of disaster recovery. Traditional storage and processing systems are unable to cope with the size of the collected data and the complexity of the applied analysis, thus distributed approaches are usually employed. In this work, we propose an open-source distributed platform that can serve as a backend for applications and services related to crisis detection and management by combining spatio-textual user generated data. The system focuses on scalability and relies on a combination of state-of-the art Big Data frameworks. It currently supports the most popular social networks, being easily extensible to any social platform. The experimental evaluation of our prototype attests its performance and scalability even under heavy load, using different query types over various cluster sizes.

Social network data publishing is dynamic, and attackers can perform association attacks based on social network directed graph data at different times. The existing social network privacy protection technology has low performance in dealing with large-scale dynamic social network directed graph data, and anonymous data publishing does not meet the needs of community structure analysis. A Dynamic Social Network Directed Graph K-In&Out-Degree Anonymity (DSNDG-KIODA) method to protect community structure is proposed. The method is based on the dynamic grouping anonymity rule to anonymize the dynamic K-in&out-degree sequence, and the virtual node distribution is added in parallel to construct an anonymous graph. The node information is transmitted based on the GraphX, and the virtual node pairs are selected and deleted according to the change of the directed graph modularity to reduce information loss. The experimental results show that the DSNDG-KIODA method improves the efficiency of processing large-scale dynamic social network directed graph data, and ensures the availability of community structure analysis when data is released.

Online social networking (SN) data is a context and time rich data stream that has showed potential for predicting suicidal ideation and behaviour. Despite the obvious benefits of this digital media, predictive modelling of acute suicidal ideation (SI) remains underdeveloped at now. In combined with robust machine learning algorithms, social networking data may provide a potential path ahead. Researchers applied a machine learning models to a previously published Instagram dataset of youths. Using predictors that reflect language use and activity inside this social networking, researchers compared the performance of the out-of-sample, cross-validated model to that of earlier efforts and used a model explanation to further investigate relative predictor relevance and subject-level phenomenology. The application of ensemble learning approaches to SN data for the prediction of acute SI may reduce the complications and modelling issues associated with acute SI at these time scales. Future research is required on bigger, more diversified populations to refine digital biomarkers and assess their external validity with more rigor

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

The article is devoted to a legal analysis of the protection of the confidentiality of children’s personal data on social networks in the context of the right to be forgotten and the General Data Protection Regulation. It is established that most social networks have their own usage rules, but their priority is to attract more users and company profit rather than the security of users’ personal data. Legal mechanisms for the implementation and protection of human rights in the digital environment are in the process of formation and improvement. It is proven that the application of the right to be forgotten has become one of the forms of personal data protection in social networks and on the Internet in general. Although initially the right to erasure emerged only at the level of the judicial practice of the Court of Justice of the EU, an effective mechanism has now been created to protect the individual’s right to delete outdated information about themselves, taking into account the balance between the right to privacy and the right to freedom of expression. Due to psychological immaturity and limited life experience, children and adolescents may casually disclose sensitive personal information, increasing the likelihood of abuse by both social networks and third parties. Therefore, minors receive special protection as vulnerable user categories according to the General Data Protection Regulation. It is argued that the incorporation of the right to be forgotten into EU legislation helps eliminate undesirable consequences of oversharing and restores minors’ ability to control their digital footprint. The terms of service of most social networks ensure additional protection of minors’ data and provide several options for deleting already disseminated information. Obstacles to the application of the right to be forgotten in social networks are identified, such as the lack of a developed mechanism for deleting photos or videos with minors published without the consent of the child or their parents by another user, the limited ability of users under 13 years old to request deletion without intermediaries, and the limited liability of social networks for the loss of information, data, and direct or indirect damages from such actions.

The traditional data query algorithm based on clustering strategy library ignores the association features of social network data, characteristic data acquisition exist a large number of redundant features and frequent relationship among features is low, resulting in the social network data query efficiency and the accuracy is poor, so a fast query algorithm for social network data based on fuzzy degree function based on association features is proposed, it is based on Apriori algorithm for data association feature mining of social network to obtain the maximum frequent association feature set; for association feature preprocessing, it reduce the maximum frequent association feature set by feature dimension reduction and de redundancy algorithm, to obtain better social network maximal frequent associated feature set; when using fuzzy function to query social network data quickly, it uses data of a single gene ambiguity function to build a fast data query diagram, input the best frequent feature set of social network, and output the query results of social network data with the highest priority. The experimental results show that the proposed algorithm has the advantages of high efficiency and high accuracy in social network data query.

Social support networks have been become one of the mostly influential metaphors in many areas around the world, especially in terms of mitigating disaster consequences and revivifying disaster affected livelihoods. More importantly, reciprocal supports ties are more powerful in healing disaster wounds of communities. Especially social capital legacies enrich by reciprocal support networks by the ways in which making may hopes among disaster victims. In this context, the objective of this study is to investigate the social support network behaviors in supporting 16 flood-affected households in the mass flooding event occurred in 2017. Particularly, the study researched social support network behaviors at different flood inundation phases such as before, during, and after. This study collected primary data (mainly social network data) using household survey and filed observations. The study used the Social Network Analysis (SNA) method for the network data analysis. This article shows that flood affected households have received social network supports in different magnitudes at before, during, and after flood inundation phases. More importantly, provision of foods, water and basic needs, sheltering, clearing contaminated households, and emotional supports have mobilized and reciprocated among victims in reviving their livelihoods. The social support network legacies have evolved at different flood inundation phases. This study fills the gabs in the flood disaster discourse on Sri Lankan context.

In today's world, trillions of persons are providing their data to social network data provider for connecting, interacting and data sharing with other users. The data provider may utilize these collected data for analysis purpose. Alternatively, multiple data providers prefer collaboration to attain enhanced analysis outcomes from the collected collaborated data. For such collaboration, the data providers do not share their data directly due to privacy issues instead they share the collected data with the trusted data publisher. The data publisher combines these collected data and subsequently publishes the data. Data collected at trusted data publisher site from multiple providers contain individuals' information that may be sensitive. Hence, the privacy of individuals may be compromised if it is published by the publisher in its original form. As a consequence, in literature, various non-cryptographic approaches are discussed for privacy-preserving collaborative social network data publishing. The motive of this paper is to emphasize the evaluation of these existing approaches with the help of different parameters.

Recently, as more and more social network data has been published in one way or another, preserving privacy in publishing social network data becomes an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Unfortunately, most of the previous studies on privacy preservation 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. 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. We show that the problem is challenging, and present a practical solution to battle neighborhood attacks. The empirical study indicates that anonymized social networks generated by our method can still be used to answer aggregate network queries with high accuracy.

Motivated by the growing practice of using social network data in credit scoring, we analyze the impact of using network-based measures on customer score accuracy and on tie formation among customers. We develop a series of models to compare the accuracy of customer scores obtained with and without network data. We also investigate how the accuracy of social network-based scores changes when consumers can strategically construct their social networks to attain higher scores. We find that those who are motivated to improve their scores may form fewer ties and focus more on similar partners. The impact of such endogenous tie formation on the accuracy of consumer scores is ambiguous. Scores can become more accurate as a result of modifications in social networks, but this accuracy improvement may come with greater network fragmentation. The threat of social exclusion in such endogenously formed networks provides incentives to low-type members to exert effort that improves everyone’s creditworthiness. We discuss implications for managers and public policy.

Influence maximization is one of the most fundamental problems in social network analysis due to its significant impact on viral marketing and targeted advertisements. Considerable amount of works has extensively studied to analyze the influence in social networks, but most of the existing works unfortunately neglected the fact that the location information in the cyber-physical world could also play an important role in the influence prorogation. Furthermore, even though a few works consider a little location information to enhance the influence maximization, they do not discuss any privacy issue of the model and expose user's location information directly to the public. This paper considers the problem of influence maximization in both GPS-enabled cyber-physical and online social networks with privacy reservation. We propose one novel model merging both GPS data of cyber-physical network and relationship data of online social network together in a unified framework, then we provide an efficient algorithm to solve the influence maximization problem in the framework. Besides the influence maximization problem, our framework could also support other applications involving both cyber-physical and online social networks. Further more, to protect the sensitive location and link information, we also provide corresponding techniques to protect the privacy during the whole influence maximization process. Empirical studies of real life datasets demonstrate the power of engaging location information to influence maximization, and suggest that our resolution outperforming most existing alternative algorithms with the protection of location privacy.

Location-based social network data offers the promise of collecting the data from a large base of users over a longer span of time at negligible costs. While several studies have applied social network data to activity and mobility analysis, a comparison with travel diaries and general statistics is lacking. In this paper we analyze geo-referenced Twitter activities from a large number of users in Singapore and its neighbouring countries. By combining this data, population statistics and travel diaries, and applying clustering techniques, we address questions regarding the detection of activity locations, the spatial separation between these locations and the transitions between these locations. Despite a large number of Twitter users present in the data set which we collected over a period of 8 months, only an amount comparable to a travel survey turned out to be useful for further analysis due to the scattered nature of the data. Kernel density estimation performs best to detect activity locations; more activity locations are detected per user than reported in the travel survey. Descriptive analysis shows that determining home locations is more difficult than detecting work locations for most planning zones. The spatial separation between detected activity locations from Twitter data and as reported in a travel survey and captured by public transport smart card data are at large similarly distributed, but also show relevant differences for very short and very long distances. This equally holds for the transitions between zones. Whether the differences between Twitter data and other data sources stem from differences in the population sub-sample, the clustering methodology or whether social networks are being used significantly more at certain locations is to be determined by further research. Despite these shortcomings, location-based social network data offers a promising data source for insights in activity locations and mobility patterns, especially for regions where travel survey data is not be readily available.

In the modern Internet era the usage of social networks such as Twitter, Instagram and Facebook is constantly increasing. The analysis of this type of data can help us understand interesting social phenomena, because these networks intrinsically capture the new nature of user interactions. Unfortunately, social network data may reveal personal and sensitive information about users, leading to privacy violations. In this paper, we propose a study of privacy risk for social network data. In particular, we empirically analyze a set of privacy attacks on social network data by using the privacy risk assessment framework PRUDEnce. After simulating the attacks on real data, we first analyze how the privacy risk is distributed over the whole population. Then, we study the effect of high-risk users sanitization on some common network metrics.KeywordsPrivacyAttack modelsSocial networks