# Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
## 7 Conclusions  
In this paper, we have presented a new privacy-preserving data mashup protocol capable of vertically integrating data partitions from multiple educational sources to satisfy the data consumers' requests without disclosing the identities of the individuals referenced in the data. Educational information to be integrated and anonymized typically comes from cloud-based e-learning environments and includes attendance to course activities, course evaluations, feedback on course materials and teaching systems, performance records, and social network data of students and instructors. Our protocol can de-identify the fused information by \(k\)-anonymizing the aggregate quasi-identifiers, resulting from the mashup of the data partitions. Unlike other privacy-preserving data mashup techniques on vertically partitioned datasets, our protocol is not linked to a particular \(k\)-anonymization method. Therefore, the protocol offers the possibility of choosing the \(k\)-anonymization method--either generalization, microaggregation or any method that satisfies the \(k\)-anonymity requirement--according to the dataset scheme and to the utility requirements of the data customers. Our protocol is capable of preventing passive adversaries, whether internal or external to the anonymization and integration process, from re-identifying individuals' sensitive data. In particular, the probability that an adversary correctly correlates the confidential attributes of an individual with their identity is \(1/k\) at most. The privacy parameter \(k\) thus determines the degree of uncertainty of the adversary. The analytic utility of the protected data is conditioned by the selected method of \(k\)-anonymization. The implementation of the proposed protocol is based on linked data, considering several separate linked data platform instances. A linked data-based implementation provides a shared architecture for linking the information contained in the different educational sources and effectively avoids ambiguity. The use of privatized and shared datasets in the Web of Data compliant with FAIR and privacy-by-design principles enables learning analytics while safeguarding the students' data privacy. With the linked data-based implementation, the datasets involved in the mashups can be semantically described, indicating which are the quasi-identifiers and the sensitive data. Thus, our mashup protocol enables the combination of datasets increasing privacy-by-design without undermining FAIR principles. All authors have contributed to the manuscript according to the following tasks: Conceptualization, M.R.-G. and J.M.D. ; methodology, M.R.-G.; validation, M.R.-G., A.B. and J.M.D. ; data curation, M.R.-G. and A.B. ; writing--original draft preparation, M.R.-G., J.M.D. and A.B. ; writing--review and editing, M.R.-G., J.M.D. and A.B. ; visualization, M.R.-G.; supervision and project administration, J.M.D. ; funding acquisition, J.M.D. All authors have read and agreed to the published version of the manuscript. The Spanish National Research Agency (AEI) funded this research through the project CREPES (ref. PID2020-115844 RB-I00) with ERDF funds. Not applicable. The Outland dataset [62] used to support the reported results can be found at [[https://analyse.kmi.open.ac.uk/open_dataset](https://analyse.kmi.open.ac.uk/open_dataset)]([https://analyse.kmi.open.ac.uk/open_dataset](https://analyse.kmi.open.ac.uk/open_dataset)) (accessed on 9 September 2021). According to their curators' description, it contains data about courses, students, and their interactions with a VLE for seven selected courses, called modules. Data illustrating the PPVD mashup protocol execution can be found in [[https://doi.org/10.5281/zenodo.5411994](https://doi.org/10.5281/zenodo.5411994)]([https://doi.org/10.5281/zenodo.5411994](https://doi.org/10.5281/zenodo.5411994)) (accessed on 3 September 2021). The authors declare no conflict of interest.