Virtual learning environments contain valuable data about students that can be correlated and analyzed to optimize learning. Modern learning environments based on data mashups that collect and integrate data from multiple sources are relevant for learning analytics systems because they provide insights into students’ learning. However, data sets involved in mashups may contain personal information of sensitive nature that raises legitimate privacy concerns. Average privacy preservation methods are based on preemptive approaches that limit the published data in a mashup based on access control and authentication schemes. Such limitations may reduce the analytical utility of the data exposed to gain students’ learning insights. In order to reconcile utility and privacy preservation of published data, this research proposes a new data mashup protocol capable of merging and k-anonymizing data sets in cloud-based learning environments without jeopardizing the analytical utility of the information. The implementation of the protocol is based on linked data so that data sets involved in the mashups are semantically described, thereby enabling their combination with relevant educational data sources. The k-anonymized data sets returned by the protocol still retain essential information for supporting general data exploration and statistical analysis tasks. The analytical and empirical evaluation shows that the proposed protocol prevents individuals’ sensitive information from re-identifying.

Sharing patient datasets curated by health institutions is critical for the advance of monitoring, surveillance and research. However, patient data is sensitive data and it can only be released under certain conditions and with previous explicit consent. Privacy preserving data sharing provides techniques to distribute datasets minimizing the risk of identification of patients. However, the sharing of datasets is typically done without considering the needs or requests of data consumers. Blockchain technologies provide an opportunity to gather those requests and share and assemble datasets using privacy-preserving methods as data and requirements on anonymity match. The architecture and design of such a solution is described, assuming an underlying permissioned blockchain network where providers such as healthcare institutions deal with consent, patient preferences and anonymity guarantees, playing a mediator role to a network of organizations.

Peer-reviewedPersonal data are of great interest in statistical studies and to provide personalized services, but its release may impair the privacy of individuals. To protect the privacy, in this paper, we present the notion and practical enforcement of semantic noise, a semantically-grounded version of the numerical uncorrelated noise addition method, which is capable of masking textual data while properly preserving their semantics. Unlike other perturbative masking schemes, our method can work with both datasets containing information of several individuals and single data. Empirical results show that our proposal provides semantically-coherent outcomes preserving data utility better than non-semantic perturbative mechanisms.Los datos personales son de gran interés en los estudios estadísticos y para proporcionar servicios personalizados, pero su lanzamiento puede poner en peligro la privacidad de los individuos. Para proteger la privacidad, en este trabajo presentamos el concepto y la aplicación práctica de ruido semántico, una versión basada en la semántica del método de adición de ruido no correlacionado numérico, que es capaz de enmascarar los datos textuales, preservando adecuadamente su semántica. A diferencia de otros sistemas de encubrimiento perturbativos, nuestro método puede trabajar con ambos conjuntos de datos que contienen información de varios individuos y los datos individuales. Los resultados empíricos muestran que nuestra propuesta proporciona resultados semánticamente coherentes preservando mejor la utilidad de los datos que los mecanismos perturbativos no semánticos.Les dades personals són de gran interès en els estudis estadístics i per proporcionar serveis personalitzats, però el seu llançament pot posar en perill la privacitat dels individus. Per protegir la privacitat, en aquest treball presentem el concepte i l'aplicació pràctica de soroll semàntic, una versió basada en la semàntica del mètode d'addició de soroll no correlacionat numèric, que és capaç d'emmascarar les dades textuals, preservant adequadament la seva semàntica. A diferència d'altres sistemes d'encobriment pertorbatius, el nostre mètode pot treballar amb dos conjunts de dades que contenen informació de diversos individus i les dades individuals. Els resultats empírics mostren que la nostra proposta proporciona resultats semànticament coherents preservant millor la utilitat de les dades que els mecanismes pertorbatius no semàntics