A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

A Study on Privacy Preserving Data Publishing With Differential Privacy

In the era of digitization it is important to preserve privacy of various sensitive information available around us, e.g., personal information, different social communication and video streaming sites' and services' own users' private information, salary information and structure of an organization, census and statistical data of a country and so on. These data can be represented in different formats such as Numerical and Categorical data, Graph Data, Tree-Structured data and so on. For preventing these data from being illegally exploited and protect it from privacy threats, it is required to apply an efficient privacy model over sensitive data. There have been a great number of studies on privacy-preserving data publishing over the last decades. Differential Privacy (DP) is one of the state of the art methods for preserving privacy to a database. However, applying DP to high dimensional tabular data (Numerical and Categorical) is challenging in terms of required time, memory, and high frequency computational unit. A well-known solution is to reduce the dimension of the given database, keeping its originality and preserving relations among all of its entities. In this thesis, we propose PrivFuzzy, a simple and flexible differentially private method that can publish differentially private data after reducing their original dimension with the help of Fuzzy logic. Exploiting Fuzzy mapping, PrivFuzzy can (1) reduce database columns and create a new low dimensional correlated database, (2) inject noise to each attribute to ensure differential privacy on newly created low dimensional database, and (3) sample each entry in the database and release synthesized database. Existing literatures show the difficulty of applying differential privacy over a high dimensional dataset, which we overcame by proposing a novel fuzzy based approach (PrivFuzzy). By applying our novel fuzzy mapping technique, PrivFuzzy transforms a high dimensional dataset to an equivalent low dimensional one, without losing any relationship within the dataset. Our experiments with real data and comparison with the existing privacy preserving models, PrivBayes and PrivGene, show that our proposed approach PrivFuzzy outperforms existing solutions in terms of the strength of privacy preservation, simplicity and improving utility. Preserving privacy of Graph structured data, at the time of making some of its part available, is still one of the major problems in preserving data privacy. Most of the present models had tried to solve this issue by coming up with complex solution, as well as mixed up with signal and noise, which make these solutions ineffective in real time use and practice. One of the state of the art solution is to apply differential privacy over the queries on graph data and its statistics. But the challenge to meet here is to reduce the error at the time of publishing the data as mechanism of Differential privacy adds a large amount of noise and introduces erroneous results which reduces the utility of data. In this thesis, we proposed an Expectation Maximization (EM) based novel differentially private model for graph dataset. By applying EM method iteratively in conjunction with Laplace mechanism our proposed private model applies differentially private noise over the result of several subgraph queries on a graph dataset. Besides, to ensure expected utility, by selecting a maximal noise level $\theta$, our proposed system can generate noisy result with expected utility. Comparing with existing models for several subgraph counting queries, we claim that our proposed model can generate much less noise than the existing models to achieve expected utility and can still preserve privacy

Anonymization procedures for tabular data: an explanatory technical and legal synthesis

In the European Union, Data Controllers and Data Processors, who work with personal data, have to comply with the General Data Protection Regulation and other applicable laws. This affects the storing and processing of personal data. But some data processing in data mining or statistical analyses does not require any personal reference to the data. Thus, personal context can be removed. For these use cases, to comply with applicable laws, any existing personal information has to be removed by applying the so-called anonymization. However, anonymization should maintain data utility. Therefore, the concept of anonymization is a double-edged sword with an intrinsic trade-off: privacy enforcement vs. utility preservation. The former might not be entirely guaranteed when anonymized data are published as Open Data. In theory and practice, there exist diverse approaches to conduct and score anonymization. This explanatory synthesis discusses the technical perspectives on the anonymization of tabular data with a special emphasis on the European Union’s legal base. The studied methods for conducting anonymization, and scoring the anonymization procedure and the resulting anonymity are explained in unifying terminology. The examined methods and scores cover both categorical and numerical data. The examined scores involve data utility, information preservation, and privacy models. In practice-relevant examples, methods and scores are experimentally tested on records from the UCI Machine Learning Repository’s “Census Income (Adult)” dataset

Privacy Preserving User Data Publication In Social Networks

Recent trends show that the popularity of Social Networks (SNs) has been increasing rapidly. From daily communication sites to online communities, an average person\u27s daily life has become dependent on these online networks. Additionally, the number of people using at least one of the social networks have increased drastically over the years. It is estimated that by the end of the year 2020, one-third of the world\u27s population will have social accounts. Hence, user privacy protection has gained wide acclaim in the research community. It has also become evident that protection should be provided to these networks from unwanted intruders. In this dissertation, we consider data privacy on online social networks at the network level and the user level. The network-level privacy helps us to prevent information leakage to third-party users like advertisers. To achieve such privacy, we propose various schemes that combine the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We combine the concepts of k-anonymity and l-diversity to achieve user privacy. To provide user-level privacy, we consider the scenario of mobile social networks as the user location privacy is the much-compromised problem. We provide a distributed solution where users in an area come together to achieve their desired privacy constraints. We also consider the mobility of the user and the network to provide much better results

Garantia de privacidade na exploração de bases de dados distribuídas

Anonymisation is currently one of the biggest challenges when sharing sensitive personal information. Its importance depends largely on the application domain, but when dealing with health information, this becomes a more serious issue. A simpler approach to avoid this disclosure is to ensure that all data that can be associated directly with an individual is removed from the original dataset. However, some studies have shown that simple anonymisation procedures can sometimes be reverted using specific patients’ characteristics, namely when the anonymisation is based on hidden key attributes. In this work, we propose a secure architecture to share information from distributed databases without compromising the subjects’ privacy. The work was initially focused on identifying techniques to link information between multiple data sources, in order to revert the anonymization procedures. In a second phase, we developed the methodology to perform queries over distributed databases was proposed. The architecture was validated using a standard data schema that is widely adopted in observational research studies.A garantia da anonimização de dados é atualmente um dos maiores desafios quando existe a necessidade de partilhar informações pessoais de carácter sensível. Apesar de ser um problema transversal a muitos domínios de aplicação, este torna-se mais crítico quando a anonimização envolve dados clinicos. Nestes casos, a abordagem mais comum para evitar a divulgação de dados, que possam ser associados diretamente a um indivíduo, consiste na remoção de atributos identificadores. No entanto, segundo a literatura, esta abordagem não oferece uma garantia total de anonimato, que pode ser quebrada através de ataques específicos que permitem a reidentificação dos sujeitos. Neste trabalho, é proposta uma arquitetura que permite partilhar dados armazenados em repositórios distribuídos, de forma segura e sem comprometer a privacidade. Numa primeira fase deste trabalho, foi feita uma análise de técnicas que permitam reverter os procedimentos de anonimização. Na fase seguinte, foi proposta uma metodologia que permite realizar pesquisas em bases de dados distribuídas, sem que o anonimato seja quebrado. Esta arquitetura foi validada sobre um esquema de base de dados relacional que é amplamente utilizado em estudos clínicos observacionais.Mestrado em Ciberseguranç

Privacy-preserving publishing of hierarchical data

Many applications today rely on storage and management of semi-structured information, for example, XML databases and document-oriented databases. These data often have to be shared with untrusted third parties, which makes individuals’ privacy a fundamental problem. In this article, we propose anonymization techniques for privacy-preserving publishing of hierarchical data. We show that the problem of anonymizing hierarchical data poses unique challenges that cannot be readily solved by existing mechanisms. We extend two standards for privacy protection in tabular data (k-anonymity and ℓ-diversity) and apply them to hierarchical data. We present utility-aware algorithms that enforce these definitions of privacy using generalizations and suppressions of data values. To evaluate our algorithms and their heuristics, we experiment on synthetic and real datasets obtained from two universities. Our experiments show that we significantly outperform related methods that provide comparable privacy guarantees

Comprehensive survey on big data privacy protection

In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals’ sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM.Published versio

Protecting privacy of semantic trajectory

The growing ubiquity of GPS-enabled devices in everyday life has made large-scale collection of trajectories feasible, providing ever-growing opportunities for human movement analysis. However, publishing this vulnerable data is accompanied by increasing concerns about individuals’ geoprivacy. This thesis has two objectives: (1) propose a privacy protection framework for semantic trajectories and (2) develop a Python toolbox in ArcGIS Pro environment for non-expert users to enable them to anonymize trajectory data. The former aims to prevent users’ re-identification when knowing the important locations or any random spatiotemporal points of users by swapping their important locations to new locations with the same semantics and unlinking the users from their trajectories. This is accomplished by converting GPS points into sequences of visited meaningful locations and moves and integrating several anonymization techniques. The second component of this thesis implements privacy protection in a way that even users without deep knowledge of anonymization and coding skills can anonymize their data by offering an all-in-one toolbox. By proposing and implementing this framework and toolbox, we hope that trajectory privacy is better protected in research