Blockchain from the Perspective of Privacy and Anonymisation: A Systematic Literature Review

The research presented aims to investigate the relationship between privacy and anonymisation in blockchain technologies on different fields of application. The study is carried out through a systematic literature review in different databases, obtaining in a first phase of selection 199 publications, of which 28 were selected for data extraction. The results obtained provide a strong relationship between privacy and anonymisation in most of the fields of application of blockchain, as well as a description of the techniques used for this purpose, such as Ring Signature, homomorphic encryption, k-anonymity or data obfuscation. Among the literature researched, some limitations and future lines of research on issues close to blockchain technology in the different fields of application can be detected. As conclusion, we extract the different degrees of application of privacy according to the mechanisms used and different techniques for the implementation of anonymisation, being one of the risks for privacy the traceability of the operations

66 Curso de Especialización en Ciberseguridad, ¿están preparados nuestros docentes?

La aparición del nuevo título de Formación Profesional, Curso de Especialización en “Ciberseguridad en entornos de tecnologías de la información”, establece el punto de partida sobre los conocimientos del profesorado en cuestiones de Ciberseguridad. Para ello hemos realizado un estudio sobre los conocimientos del profesorado que imparte docencia en la formación profesional, basado en la propuesta curricular en ciberseguridad de la guía ACM/IEEE/AIS SIGSEC/IFIP Cybersecurity, con el objetivo de proponer un itinerario formativo en Ciberseguridad para el profesorado, de forma que estén en disposición de ofrecer una mejor respuesta y mayor calidad ante el proceso de formación de los profesionales del futuro en dicha materia. En primer lugar hemos desarrollado un estudio basado cuestionarios, a través del cual se han presentado las unidades de conocimiento en materia de Ciberseguridad al profesorado de Andalucía y sobre las que han realizado la valoración de su conocimiento en dicha materia. Se presenta un análisis cuantitativo de los resultados obtenidos priorizando las necesidades formativas. Como conclusión de nuestro estudio hemos propuesto la elaboración de un itinerario formativo para el profesorado basado en las diez unidades de conocimiento

Data curation in the Internet of Things: A decision model approach

Current Internet of Things (IoT) scenarios have to deal with many challenges especially when a large amount of heterogeneous data sources are integrated, that is, data curation. In this respect, the use of poor-quality data (i.e., data with problems) can produce terrible consequence from incorrect decision-making to damaging the performance in the operations. Therefore, using data with an acceptable level of usability has become essential to achieve success. In this article, we propose an IoT-big data pipeline architecture that enables data acqui sition and data curation in any IoT context. We have customized the pipeline by including the DMN4DQ approach to enable us the measuring and evaluat ing data quality in the data produced by IoT sensors. Further, we have chosen a real dataset from sensors in an agricultural IoT context and we have defined a decision model to enable us the automatic measuring and assessing of the data quality with regard to the usability of the data in the contextMinisterio de Ciencia y Tecnología RTI2018-094283-B-C3

ELI: an IoT-aware big data pipeline with data curation and data quality

The complexity of analysing data from IoT sensors requires the use of Big Data technologies, posing challenges such as data curation and data quality assessment. Not facing both aspects potentially can lead to erroneous decision-making (i.e., processing incorrectly treated data, introducing errors into processes, causing damage or increasing costs). This article presents ELI, an IoT-based Big Data pipeline for developing a data curation process and assessing the usability of data collected by IoT sensors in both offline and online scenarios. We propose the use of a pipeline that integrates data transformation and integration tools and a customisable decision model based on the Decision Model and Notation (DMN) to evaluate the data quality. Our study emphasises the importance of data curation and quality to integrate IoT information by identifying and discarding low-quality data that obstruct meaningful insights and introduce errors in decision making. We evaluated our approach in a smart farm scenario using agricultural humidity and temperature data collected from various types of sensors. Moreover, the proposed model exhibited consistent results in offline and online (stream data) scenarios. In addition, a performance evaluation has been developed, demonstrating its effectiveness. In summary, this article contributes to the development of a usable and effective IoT-based Big Data pipeline with data curation capabilities and assessing data usability in both online and offline scenarios. Additionally, it introduces customisable decision models for measuring data quality across multiple dimensions.Ministerio de Ciencia e Innovación (MICIN) España AEI/10.13039/50110001103

ELI: an IoT-aware big data pipeline with data curation and data quality

The complexity of analysing data from IoT sensors requires the use of Big Data technologies, posing challenges such as data curation and data quality assessment. Not facing both aspects potentially can lead to erroneous decision-making (i.e., processing incorrectly treated data, introducing errors into processes, causing damage or increasing costs). This article presents ELI, an IoT-based Big Data pipeline for developing a data curation process and assessing the usability of data collected by IoT sensors in both offline and online scenarios. We propose the use of a pipeline that integrates data transformation and integration tools and a customisable decision model based on the Decision Model and Notation (DMN) to evaluate the data quality. Our study emphasises the importance of data curation and quality to integrate IoT information by identifying and discarding low-quality data that obstruct meaningful insights and introduce errors in decision making. We evaluated our approach in a smart farm scenario using agricultural humidity and temperature data collected from various types of sensors. Moreover, the proposed model exhibited consistent results in offline and online (stream data) scenarios. In addition, a performance evaluation has been developed, demonstrating its effectiveness. In summary, this article contributes to the development of a usable and effective IoT-based Big Data pipeline with data curation capabilities and assessing data usability in both online and offline scenarios. Additionally, it introduces customisable decision models for measuring data quality across multiple dimensions