45 research outputs found

    Studying the Utility Preservation in Social Network Anonymization via Persistent Homology

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    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

    PHDP: Preserving Persistent Homology in Differentially Private Graph Publications

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    Online social networks (OSNs) routinely share and analyze user data. This requires protection of sensitive user information. Researchers have proposed several techniques to anonymize the data of OSNs. Some differential-privacy techniques claim to preserve graph utility under certain graph metrics, as well as guarantee strict privacy. However, each graph utility metric reveals the whole graph in specific aspects.We employ persistent homology to give a comprehensive description of the graph utility in OSNs. This paper proposes a novel anonymization scheme, called PHDP, which preserves persistent homology and satisfies differential privacy. To strengthen privacy protection, we add exponential noise to the adjacency matrix of the network and find the number of adding/deleting edges. To maintain persistent homology, we collect edges along persistent structures and avoid perturbation on these edges. Our regeneration algorithms balance persistent homology with differential privacy, publishing an anonymized graph with a guarantee of both. Evaluation result show that the PHDP-anonymized graph achieves high graph utility, both in graph metrics and application metrics

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

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    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

    Exploring the integration of traditional and molecular epidemiological methods for infectious disease outbreaks

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    BACKGROUND: Understanding the transmission dynamics of infectious pathogens is critical to developing effective public health strategies. Traditionally, time consuming epidemiological methods were used, often limited by incomplete or inaccurate datasets. Novel phylogenetic techniques can determine transmission events, but have rarely been used in real-time outbreak settings to inform interventions and limit the impact of outbreaks. METHODS: I undertook a series of novel studies to explore the utility of combining phylogenetics with traditional epidemiological analysis to enhance the understanding of transmission dynamics. I investigated HIV in an endemic South African setting and Ebola in an acute outbreak in Sierra Leone. The strengths and limitations of this combined approach are explored, ethical issues investigated and recommendations made regarding the implications of this work for public health. RESULTS: Phylogenetics provides an exciting and synergistic tool to epidemiological analysis in outbreak investigation and control. These combined methods enable a more detailed understanding than is possible through either discipline alone. My key findings include: • Identification of infection source: Phylogenetics gives new insight into the role of external introductions (e.g. migrators) in driving and sustaining the high incidence of HIV. • Earlier identification of new emerging clusters: I identified a new cluster of HIV from around a mining community. This is one of the first examples of molecular methods detecting a previously unknown outbreak. • Identification of novel mechanisms of transmission: This work suggests that children may have been infected by playing in puddles contaminated with Ebola, a previously unrecognised route of transmission. CONCLUSION: The integration of these two methods facilitate sophisticated real-time techniques to maximise understanding of transmission dynamics, allowing faster and more effectively targeted interventions. Moving forwards, sequence data should be incorporated into standard outbreak investigation. This is critical at a time when infectious disease outbreaks have led to the some of the most significant global health threats of the recent past

    Three Risky Decades: A Time for Econophysics?

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    Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era

    Machine Learning Methods with Noisy, Incomplete or Small Datasets

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    In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

    Preface

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    Dynamics of High-Resolution Networks

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