# Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
## 1 Introduction  
Aware of data opportunities in an information-driven world, private, academic, and government organizations are including frameworks that enable the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) on Big Data Governance and Metadata Management in their roadmap [1]. In particular, the IEEE Standards Association states the need for devising interoperable data architectures that enable effective integration--i.e., _mashup_--of data from multiple sources to answer specific information requests [2]. Different data integration approaches from diverse application domains attempt to cover the requirement of interoperability by providing public data access infrastructures that enable dataset mashups [2], such as the Common Access Platform, proposed by the National Institute of Standards and Technology (NIST) [3, 4, 5]. By unifying information from diversified data repositories, companies and organizations can generate value-added information and, consequently, detect new business opportunities, identify risks, and discover new patterns and insights. A great variety of sectors can benefit from Big Data integration to empower their analytic systems, including social media and search engines; insurance, banking, and finances; marketing; retail and point-of-sale analytics; manufacturing optimization; transportation; utility and energy; healthcare; and research and development [6]. In educational institutions, large amounts of ubiquitous data about students, available from different cloud-based data sources [7], include students' demographics besides relevant data about students' learning. They can be merged with data from the institution's student records, digital libraries, and Learning Management Systems (LMS) to build customized Virtual Learning Environments (VLE) as personal learning mashups [8]. Thanks to the simplicity and low complexity of web standards, the first generation of personal learning mashups have been based on a composition of web-based educational services [9]. However, the automation of independent remote services orchestration is not straightforward, yielding an obstacle for implementing and using service-based learning mashups [10]. In contrast, new generation VLEs are based on mashing up data available in realistic cloud-based learning environments [7], which may even involve data from independent educational institutions [11]. Eventually, Learning Analytics (LA) systems built on new generation learning mashups can benefit from the fusion of large amounts of data gathered from cloud-based learning environments and institutional teaching systems to obtain new insights that can improve teachers' learning design practices [12] and students' learning performance [13]. Supporting data mashups is critical to assist in data-driven decision-making. However, sharing and mashing up information with personal content may compromise the privacy of individuals referenced in the data. Regulations on data privacy, such as the Family Educational Rights and Privacy Act (FERPA) [14], point out that shared data with unique identifiers removed can still lead to the re-identification of individuals through data linkage attacks [15] by correlating potentially identifying combinations of attributes--called Quasi-Identifiers (QI)--with publicly available external information. Consequently, and since the trust of the educational community is essential for the adoption of LA, there is a need to incorporate anonymization mechanisms in LA that guarantee information privacy [13]. More generally, the NIST identifies the balance between privacy and utility as one of the main issues to be addressed in interoperable data architectures [16]. The dual need to share information and protect privacy at the same time has been extensively addressed in the field of Privacy-Preserving Data Publishing (PPDP) [15; 17] and, more recently, in e-learning systems [18].