Social Network Anonymization via Local-perturbing Approach

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.

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the Court of Justice of the EU, an effective mechanism has now been created to protect the individual’s right to delete outdated information about themselves, taking into account the balance between the right to privacy and the right to freedom of expression.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 Quest for Information Privacy in Africa: A Review Essay

Personal big data pricing method based on differential privacy

У статті здійснено когнітивне моделювання розвитку загроз приватності особистості та безпеці країни. Для моделювання обрано такі фактори: «Приватність особистості», «Безпека розвитку країни», «Розробка систем захисту», «Онлайн-присутність», «Кібер-атаки», «Втрати від кіберзлочинів», «Витрати на захист», «Сталий розвиток». Результати показали, що всі фактори в системі є дестабілізаційними й чутливими до змін. Найбільш чутливим є фактор «Онлайн-присутність». Такий характер системи свідчить про те, що будь-які зміни можуть дестабілізувати систему, привести до змін усі інші фактори. При цьому система сама сприятиме таким змінам, оскільки є нестійкою. Отже, уся система забезпечення захисту приватності особистості та безпеки країни потребує постійної уваги й моніторингу змін усіх факторів. Фактори «Приватність особистості» та «Безпека розвитку країни» є взаємозалежними, оскільки демонструють майже однаковий вплив на них усіх інших факторів системи. Так, ці фактори підсилюватимуться при зростанні факторів «Розробка систем захисту», «Онлайн-присутність», «Витрати на захист» і «Сталий розвиток». Зростання рівня захисту приватності особистості сприятиме зростанню рівня безпеки країни й навпаки. При цьому «Приватність особистості» сильніше впливає на зміцнення рівня безпеки країни, порівняно з впливом безпеки держави на захист приватності особистості при її присутності в мережі Інтернет. Лише два фактори – «Кібератаки» та «Втрати від кіберзлочинів» зменшуватимуть рівень захисту приватностій безпеки країни при їх зростанні. Рівень захисту приватності є більш чутливим до впливу на неї з боку кібератак, порівняно з чутливістю рівня безпеки країни. На рівень безпеки розвитку держави найбільше впливає рівень захисту приватності особистості, і цей вплив на 14 % більш потужний, порівняно з протилежним впливом факторів. Аналіз засвідчив, що основним пріоритетом під час розвитку мережевих технологій, упровадженні ІКТ у всі сфери суспільного життя та розвитку інформаційної економіки повинно бути забезпечення захисту приватності особистості, її персональних даних. Забезпечення безпеки розвитку країни потребуватиме більшого рівня розвитку систем захисту, порівняно із захистом приватності особистості, при цьому на 4 % менше впливатиме на сталий розвиток.

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.

Privacy is an important component of freedom and plays a key role in protecting fundamental human rights. It is becoming increasingly difficult to ignore the fact that without appropriate levels of privacy, a person’s rights are diminished. Users want to protect their privacy - particularly in “privacy invasive” areas such as social networks. However, Social Network users seldom know how to protect their own privacy through online mechanisms. What is required is an emerging concept that provides users legitimate control over their own personal information, whilst preserving and maintaining the advantages of engaging with online services such as Social Networks. This paper reviews “Privacy by Design (PbD)” and shows how it applies to diverse privacy areas. Such an approach will move towards mitigating many of the privacy issues in online information systems and can be a potential pathway for protecting users’ personal information. The research has also posed many questions in need of further investigation for different open source distributed Social Networks. Findings from this research will lead to a novel distributed architecture that provides more transparent and accountable privacy for the users of online information systems.

With the constant increase of social-network data published, the privacy preservation becomes more and more important. Although some literature algorithms apply K-anonymity to the relational data to prevent an adversary from significantly perpetrating privacy breaches, the inappropriate choice of K has a big impact on the quality of privacy protection and data utility. We propose a technique named Relationship Privacy Preservation based on Compressive Sensing (RPPCS) in this paper to anonymize the relationship data of social networks. The network links are randomized from the recovery of the random measurements of the sparse relationship matrix to both preserve the privacy and data utility. Two comprehensive sets of real-world relationship data on social networks are applied to evaluate the performance of our anonymization technique. Our performance evaluations based on Collaboration Network and Gnutella Network demonstrate that our scheme can better preserve the utility of the anonymized data compared to peer schemes. Privacy analysis shows that our scheme can resist the background knowledge attack.

Social network data has been increasingly made publicly available and analyzed in a wide spectrum of application domains. The practice of publishing social network data has brought privacy concerns to the front. Serious concerns on privacy protection in social networks have been raised in recent years. Realization of the promise of social networks data requires addressing these concerns. This paper considers the privacy disclosure in social network data publishing. In this paper, we present a systematic analysis of the various risks to privacy in publishing of social network data. We identify various attacks that can be used to reveal private information from social network data. This information is useful for developing practical countermeasures against the privacy attacks.KeywordsPrivacy disclosureSocial networksThreat analysisData publications

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.

Addressing the policy challenges and opportunities of “Big data”

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.

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.

Differential privacy protection scheme based on community density aggregation and matrix perturbation

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.