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.

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.

Current IoT scenarios have to deal with many challenges especially when a large amount of heterogeneous data sources are integrated, i.e., 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 paper, we propose an IoT-Big Data Pipeline architecture that enables data acquisition and data curation in any IoT context. We have customised the pipeline by including the DMN4DQ approach to enable us the measuring and evaluating 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 context.