# Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
## 1 Introduction
A variety of PPDP methods have been proposed to mitigate the risk of re-identifying the data subjects and, in turn, yield protected data that is still useful for specific statistical analyses. Some well-known methods, such as microaggregation [19] or generalization [20], enable data \(k\)-anonymization. By \(k\)-anonymizing a dataset, each QI is altered--or masked--to make it indistinguishable from the QIs of other \(k-1\) individuals, thereby reducing the probability of re-identification to \(1/k\)[20]. The \(k\) parameter is used to control the masking level: the higher the \(k\), the higher the masking level--and, consequently, the greater the anonymity degree--but the less valuable the anonymized information will be for statistical analysis. Traditional PPDP techniques that are satisfactory to anonymize single datasets may not be suitable to preserve privacy in the context of data mashup. Privacy-preserving data mashup alternatives not only have to make the integrated dataset satisfy the established privacy requirement, but they also have to face new challenges: (i) the parties involved in the data integration and anonymization process may learn more individuals' information than is disclosed in the integrated and anonymized dataset; (ii) if different attributes from different repositories about the same set of individuals are mashed up--vertically integrated data--a privacy-aware common identifying attribute is required to serve as a link or connector in the integration process; and (iii) mashing up datasets adds new meaning to information that is not available in the individual datasets. These factors may increase the possibility of identifying the individuals' records. PPDP techniques studied in the field of LA [18; 21] have not taken into account the distributed nature of learning datasets. Research Contribution  
In this paper, we propose a new _privacy-preserving vertical data (PPVD)__mashup protocol_ capable of: (i) attending to requests for learning datasets from data consumers; (ii) identifying the learning data sources, i.e., the set of data providers, that can satisfy a particular data request; (iii) vertically integrating learning data from the different educational sources without disclosing the identities of the (students) individuals referenced in the data and \(k\)-anonymizing the quasi-identifiers of the integrated dataset; and (iv) providing the resulting \(k\)-anonymized dataset to the data consumer. The protocol integrates learning data effectively and constitutes a Privacy-by-Design (PbD) solution for interoperable data architectures in the educational sphere, reconciling LA with privacy. The protocol can be adopted in any field of application beyond LA systems. Unlike other privacy-preserving data mashup techniques, our protocol is not linked to a particular \(k\)-anonymization method. As stated in [14; 22], no particular anonymization method is universally the best option for every dataset. Each method has benefits and drawbacks with respect to expected applications of the information. The PPVD mashup protocol offers the possibility of choosing the \(k\)-anonymization method--either generalization, microaggregation, or any other method that satisfies the \(k\)-anonymity requirement--according to the nature of the dataset and the utility requirements of the data customers. We address anonymity in the context of data mashups in terms of unlinkability [23; 24] and de-identification [14]. Specifically, we analytically and empirically prove that the proposed protocol is capable of (i) unlinking sensitive data from QIs and (ii) de-identifying sensitive data, thereby preventing adversaries from uniquely associating the sensitive data of a specific (student) individual with their identity. The PPVD mashup protocol defines a solution to reconcile the seemingly discordant PbD principles [25] and FAIR principles [26]. FAIR principles are essential in the Web of Data and the Knowledge Graph [27], which aim at building wide-scale information systems to share large amounts of data.