Data publishing
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Big data privacy: a technological perspective and review Open
Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results.…
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Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey Open
Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it t…
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Publishing Graph Degree Distribution with Node Differential Privacy Open
Graph data publishing under node-differential privacy (node-DP) is challenging due to the huge sensitivity of queries. However, since a node in graph data oftentimes represents a person, node-DP is necessary to achieve personal data protec…
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Trajectory Recovery From Ash Open
Human mobility data has been ubiquitously collected through cellular networks and mobile applications, and publicly released for academic research and commercial purposes for the last decade. Since releasing individual's mobility records u…
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A comprehensive review on privacy preserving data mining Open
Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespre…
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FPDP: Flexible Privacy-Preserving Data Publishing Scheme for Smart Agriculture Open
Food security is a global concern. Benefit from the development of 5G, IoT is used in agriculture to help the farmers to maintain and improve productivity. It not only enables the customers, both at home and abroad, to become more informed…
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Are Scientific Data Repositories Coping with Research Data Publishing? Open
Research data publishing is intended as the release of research data to make it possible for practitioners to (re)use them according to "open science" dynamics. There are three main actors called to deal with research data publishing pract…
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Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data Open
Human mobility data has been ubiquitously collected through cellular networks and mobile applications, and publicly released for academic research and commercial purposes for the last decade. Since releasing individual's mobility records u…
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A Survey on Privacy in Social Media Open
The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. These data provide opportunities for re…
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Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation Open
Personalized recommendation is crucial to help users find pertinent information. It often relies on a large collection of user data, in particular users' online activity (e.g., tagging/rating/checking-in) on social media, to mine user pref…
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Balancing Open Science and Data Privacy in the Water Sciences Open
Open science practices such as publishing data and code are transforming water science by enabling synthesis and enhancing reproducibility. However, as research increasingly bridges the physical and social science domains (e.g., socio‐hydr…
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ECA: An Edge Computing Architecture for Privacy-Preserving in IoT-Based Smart City Open
Recently, IoT has greatly influenced our daily lives through various applications. One of the most promising application is smart city that leverages IoT devices to manage cities without any human intervention. The high possibility of sens…
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Improving transparency and scientific rigor in academic publishing Open
Progress in basic and clinical research is slowed when researchers fail to provide a complete and accurate report of how a study was designed, executed, and the results analyzed. Publishing rigorous scientific research involves a full desc…
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Attribute-centric anonymization scheme for improving user privacy and utility of publishing e-health data Open
The adoption of advanced technologies in the healthcare sector has brought about many improvements in the industry, including better communication between healthcare providers, improved quality of treatment, and reduced cost. For the most …
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Decentralized provenance-aware publishing with nanopublications Open
Publication and archival of scientific results is still commonly considered the responsability of classical publishing companies. Classical forms of publishing, however, which center around printed narrative articles, no longer seem well-s…
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Developing a Research Data Policy Framework for All Journals and Publishers Open
Abstract:More journals and publishers - and funding agencies and institutions - are introducing research data policies. But as the prevalence of policies increases, there is potential to confuse researchers and support staff with numerous …
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When are researchers willing to share their data? – Impacts of values and uncertainty on open data in academia Open
Grounded in the value-based theory, this article proclaims that most individuals in academia embrace open data when the perceived advantages outweigh the disadvantages. Furthermore, uncertainty factors impact the perceived value (consistin…
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Improved Generalization for Secure Data Publishing Open
In data publishing, privacy and utility are essential for data owners and users respectively, which cannot coexist well. This incompatibility puts the data privacy researchers under an obligation to find newer and reliable privacy preservi…
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A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing Open
Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual incl…
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Improving transparency and scientific rigor in academic publishing Open
Progress in basic and clinical research is slowed when researchers fail to provide a complete and accurate report of how a study was designed, executed, and the results analyzed. Publishing rigorous scientific research involves a full desc…
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Strategies and guidelines for scholarly publishing of biodiversity data Open
The present paper describes policies and guidelines for scholarly publishing of biodiversity and biodiversity-related data, elaborated and updated during the Framework Program 7 EU BON project, on the basis of an earlier version published …
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Publishing and sharing multi-dimensional image data with OMERO Open
Imaging data are used in the life and biomedical sciences to measure the molecular and structural composition and dynamics of cells, tissues, and organisms. Datasets range in size from megabytes to terabytes and usually contain a combinati…
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SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees Open
Methods for privacy-preserving data publishing and analysis trade off privacy risks for individuals against the quality of output data. In this article, we present a data publishing algorithm that satisfies the differential privacy model. …
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Differential Privacy for Data and Model Publishing of Medical Data Open
Combining medical data and machine learning has fully utilized the value of medical data. However, medical data contain a large amount of sensitive information, and the inappropriate handling of data can lead to the leakage of personal pri…
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Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes Open
The publication of a patient’s dataset is essential for various medical investigations and decision-making. Currently, significant focus has been established to protect privacy during data publishing. The existing privacy models for multip…
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Improved l-diversity: Scalable anonymization approach for Privacy Preserving Big Data Publishing Open
In the era of big data analytics, data owner is more concern about the data privacy. Data anonymization approaches such as k-anonymity, l-diversity, and t-closeness are used for a long time to preserve privacy in published data. However, t…
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Achieving Data Truthfulness and Privacy Preservation in Data Markets Open
As a significant business paradigm, many online information platforms have\nemerged to satisfy society's needs for person-specific data, where a service\nprovider collects raw data from data contributors, and then offers value-added\ndata …
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A Weather-Aware Framework for Population Mobility Modelling (Short Paper) Open
The widespread availability of GPS-enabled mobile devices has contributed towards an unprecedented volume of data on human movement. Human mobility data are the key input for developing accurate mobility models that can support decision-ma…
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Lightweight Privacy-Preserving Raw Data Publishing Scheme Open
Data publishing or data sharing is an important part of analyzing network environments and improving the Quality of Service (QoS) in the Internet of Things (IoT). In order to stimulate data providers (i.e., IoT end-users) to contribute the…
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Between Access and Privacy: Challenges in Sharing Health Data Open
Objective: To summarize notable research contributions published in 2017 on data sharing and privacy issues in medical informatics. Methods: An extensive search of PubMed/Medline, Web of Science, ACM Digital Library, IEEE Xplore, and AAAI …