Social Network Anonymization Research Articles

Disinformation and Fact-Checking in the Face of Natural Disasters: A Case Study on Turkey–Syria Earthquakes

Natural disasters linked to contexts of unpredictability and surprise generate a climate of uncertainty in the population, resulting in an exponential increase in disinformation. These are crisis situations that cause the management of public and governmental institutions to be questioned, diminish citizens’ trust in the media, and reinforce anonymity in social networks. New digital algorithms create a scenario plagued by fake news and levels of viralization of rumors never before contemplated. Our objective is to analyze the verification capacity of fact-checking agencies at X at times of information disorder, such as the Turkey–Syria earthquakes in 2023. We apply a mixed methodology of comparative content analysis to government, news agency, and IFCN accounts, generating a general sample (n = 46,747) that is then subjected to thematic categorization to create a specific sample (n = 564). The results indicate a low commitment to fact-checking on the part of official bodies and news agencies, as opposed to fact-checking agencies’ accurate handling of the facts. The lack of debate and engagement generated by digital audiences in the face of the discursive intentionality of disinformation is significant.

HAkAu: hybrid algorithm for effective k-automorphism anonymization of social networks

Online social network datasets contain a large amount of various information about their users. Preserving users’ privacy while publishing or sharing datasets with third parties has become a challenging problem. The k-automorphism is the anonymization method that protects the social network dataset against any passive structural attack. It provides a higher level of protection than other k-anonymity methods, including k-degree or k-neighborhood techniques. In this paper, we propose a hybrid algorithm that effectively modifies the social network to the k-automorphism one. The proposed algorithm is based on the structure of the previously published k-automorphism KM algorithm. However, it solves the NP-hard subtask of finding isomorphic graph extensions with a genetic algorithm and employs the GraMi algorithm for finding frequent subgraphs. In the design of the genetic algorithm, we introduce the novel chromosome representation in which the length of the chromosome is independent of the size of the input network, and each individual in each generation leads to the k-automorphism solution. Moreover, we present a heuristic method for selecting the set of vertex disjoint subgraphs. To test the algorithm, we run experiments on a set of real social networks and use the SecGraph tool to evaluate our results in terms of protection against deanonymization attacks and preserving data utility. It makes our experimental results comparable with any future research.

A fast graph modification method for social network anonymization

Privacy on social networks is one of the most important and well-known issues. Various algorithms have been proposed to preserve the privacy of social network, all of which try to change the graph structure such that the utility of the graph is maintained. Although these algorithms have been successful in protecting the privacy and the utility of social networks, they are not suitable for anonymizing big data, because of the high cost of processing. Some of these algorithms have a high runtime. In addition, they should be improved from the aspect of preserving graph utility. In this paper, an effective algorithm has been introduced to increase the anonymization speed, as well as improving the graph utility. This algorithm uses number factorization to remove the best edges from the graph in the graph modification step of the algorithm. Since the appropriate edges are selected just through one scan of the edges, the runtime is reduced. In order to add edges to the graph, all the appropriate edges are selected simultaneously and added to the graph using NaFa algorithm. Some limitations are applied to the selection of edges to choose the correct ones. Taking into account the appropriate criteria to select edges in both the removal and addition steps improves the graph utility. The evaluation results of the proposed algorithm on real data sets show the efficiency of the algorithm to sharply reduce the runtime and increase the graph utility, simultaneously.

Anonymity in social networks: the case of anonymous social media

The anonymity provided by the so-called anonymous social networks is studied in this paper. More precisely, emphasis is given on investigating whether the underlying personal data processing in such networks may suffice to result in tracking or identification of the users. To this end, five popular anonymous smart applications are being analysed through monitoring the outgoing traffic of Android devices in real-time when using these applications, to examine which personal data - including device data - are being processed by either the anonymous networks or third parties such as library providers. The corresponding privacy policies of these applications are also examined, towards evaluating whether the information provided to the users is sufficient. Our analysis concludes that there is personal data processing in place even in such (so-called anonymous) applications which in turn implies that users anonymity cannot ensured, whilst the corresponding privacy policies may leave room for further improvement.

Anonymity in social networks: The case of anonymous social media

The anonymity provided by the so-called anonymous social networks is studied in this paper. More precisely, emphasis is given on investigating whether the underlying personal data processing in such networks may suffice to result in tracking or identification of the users. To this end, five popular anonymous smart applications are being analysed through monitoring the outgoing traffic of Android devices in real-time when using these applications, to examine which personal data - including device data - are being processed by either the anonymous networks or third parties such as library providers. The corresponding privacy policies of these applications are also examined, towards evaluating whether the information provided to the users is sufficient. Our analysis concludes that there is personal data processing in place even in such (so-called anonymous) applications which in turn implies that users anonymity cannot ensured, whilst the corresponding privacy policies may leave room for further improvement.

Studying the utility preservation in social network anonymization via persistent homology

Following the trend of preserving privacy in online-social-network publishing, various anonymization mechanisms have been designed and applied. Differential privacy is an approach that guarantees the privacy level. Many existing mechanisms claim that they can also preserve the utility very well during anonymization. However, their utility analysis is always based on some specifically chosen metrics. While the existing metrics only partially present the graph utility, this paper aims to find a novel approach that describes the network in multiple scales. Persistent homology is a high-level metric, in that it reveals the parameterized topological features with various scales, and it is applicable for real-world applications. In this paper, four differential privacy mechanisms with different abstraction models are analyzed with traditional graph metrics and with persistent homology. The evaluation results demonstrate that all algorithms can partially or conditionally preserve certain graph utilities, but none of them are suitable for all metrics. Furthermore, none of the existing mechanisms fully preserves persistent homology, especially in high dimensions, which implies that the true graph utility is lost.

Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks.

Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks.

Alpha-anonymization techniques for privacy preservation in social networks

Rapid growth and development of social networks has attracted the interest of the scientific community to utilize these huge datasets for research purpose. However, preserving the privacy of the users in the published data has also become an important concern. An adversary with very little background knowledge about the actors can extract personal information from the published data. To prevent such type of attacks, different anonymization models have been proposed for relational micro-data, which are further extended and adjusted to handle social network data. Preserving the structural properties of the raw graph is one of the most important aspects of social network anonymization. In this paper, we propose an (α, k) anonymity model based on the eigenvector centrality of the nodes present in the raw graph. We further extend the (α, k) anonymity model to propose (α, l) diversity model and (α, c, l) diversity model, which can also protect the sensitive attribute values associated with a particular actor. For anonymization purpose, we applied the noise node addition technique to generate the anonymized graphs. We tested our proposed algorithms with both synthetic dataset and real dataset. The results obtained show the effectiveness of our proposed algorithm in preserving the structural property of the raw graph. Our proposed methods add noise nodes efficiently so that they have minimal social importance.

Privacy preserving anonymization of social networks using eigenvector centrality approach

Privacy preserving anonymization of social networks using eigenvector centrality approach

Constrained obfuscation of relational databases

The need to share data often conflicts with privacy preservation. Data obfuscation attempts to overcome this conflict by modifying the original data while optimizing both privacy and utility measures. In this paper we introduce the concept of Constrained Obfuscation Problems (COPs) which formulate the task of obfuscating data stored in relational databases. The main idea behind COPs is that many obfuscation scenarios can be modeled as a data generation process which is constrained by a predefined set of rules. We demonstrate the flexibility of the COP definition by modeling several different obfuscation scenarios: Production Data Obfuscation for Application Testing (PDOAT), anonymization of relational data, and anonymization of social networks. We then suggest a general approach for solving COPs by reducing them into a set of Constrained Satisfaction Problems (CSPs). Such reduction enables the employment of the well-studied CSP framework in order to solve a wide range of complex rules. Some of the resulting CSPs may contain a large number of variables, which may make them intractable. In order to overcome such intractability issues, we present two useful heuristics that decompose such large CSPs into smaller tractable sub-CSPs. We also show how the well-known ℓ-diversity privacy measure can be incorporated into the COP framework in order to evaluate the privacy level of COP solutions. Finally, we evaluate the new method in terms of privacy, utility and execution time.

Research of Privacy-Preserving for Graph Mining of Government Affairs Office Automation Systems

Against the problems of privacy leakage from the graph of workflow structure of the government affair OA systems, the paper puts forwards a privacy preserving model which is effective in the protection of degrees and workflow amounts of the nodes in the government business workflow graph. For further implementation, it proposes a new unified anonymization algorithm oriented the actual nodes in the workflow graph based on the ides of social network anonymization and a new method of random weights disturbing to prevent the important nodes in the graph from being recognized by attackers and protect the information of workflow amount in the government and public institutions. At the end of this paper, the model has been verified by two experimentations that proves its feasibility and usability.

SACK: Anonymization of Social Networks by Clustering of K-edge-connected Subgraphs

In this paper, a method for anonymization of social networks by clustering of k-edge-connected subgraphs (SACK) is presented. Previous anonymization algorithms do not consider distribution of nodes in social network graph according to their attributes. SACk tries to focus on this aspect that probability of existence of an edge between two nodes is related to their attributes and this leads to a graph with connected subgraphs. Using connected subgraphs in anonymization process this method obtains better experimental results both in data quality and time. Sequential clustering is used for anonymization using k-edge connected subgraphs for starting step. Sequential clustering is a greedy algorithm and results are dependent on starting point.

Complexity of social network anonymization

With an abundance of social network data being released, the need to protect sensitive information within these networks has become an important concern of data publishers. To achieve this objective, various notions of k-anonymization have been proposed for social network graphs. In this paper we focus on the complexity of optimization problems that arise from trying to anonymize graphs, establishing that optimally k-anonymizing the label sequences of edge-labeled graphs is intractable. We show how this result implies intractability for other notions of k-anonymization in literature. We also consider the case of bipartite social network graphs which arise from the representation of distinct entities, such as movies and viewers, patients and drugs, or products and customers. Within this setting we demonstrate that, although k-anonymizing edge-labeled graphs is intractable for k ≥ 3, polynomial time algorithms exist for arbitrary bipartite graphs when k = 2 and for unlabeled bipartite graphs irrespective of the value of k. Finally, in this paper we extend the study of attribute disclosure within the context of social networks by defining t-closeness, a measure of how effectively an adversary can determine sensitive information about members of a k-anonymous social network.