# Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
## 2 Related Works
### Privacy-Preserving Data Mashup  
Approaches on privacy-preserving data mashups address the integration of _horizontally partitioned_ and _vertically partitioned_ datasets, each data partition being held by a different data provider. In the horizontally partitioned datasets, all the data partitions follow the same data schema, i.e., all the data partitions register the same attributes, but each partition contains records of different individuals. A typical scenario of horizontal partition is one in which the data providers are individuals supplying their data, e.g., demographic, health, and exercise data. Privacy-preserving data mashups on horizontally partitioned datasets have been extensively addressed [32; 33; 34]. These strategies have in common the segregation of data in the collection process. In the first phase, the data providers send the quasi-identifiers to the data collector, also known as the mashup coordinator. With this information, the mashup coordinator \(k\)-anonymizes the set of received quasi-identifiers and distributes the masked quasi-identifiers to the data providers. In the following phase, the confidential attributes are sent to the mashup coordinator along with the masked quasi-identifiers. This segregated collection contributes to anonymizing data because it disassociates confidential attributes from original quasi-identifiers. Unlike previous protocols that output \(k\)-anonymized data, [PERSON] et al. [35] presents a perturbative mashup protocol that provides noisy anonymized data to train distributed machine learning models. Data perturbation is caused by geometric data transformations, randomized expansion noise addition, and data shuffling. In scenarios of vertically partitioned datasets, data providers record different features on the same set of individuals, i.e., each vertical partition registers a different set of attributes and, thus, follows a different data schema. It is assumed that all the vertical partitions have a common identifier attribute, which will be used as a connector to integrate the partitions. Vertical partitioning of data is also a data distribution model often found in real cases, such as healthcare [36], the financial sector [37], or one-stop services [38]. Vertical partitioning is the typical configuration of the datasets used to build the next-generation VLEs. Databases used to store and query e-learning data can be implemented with diverse storage techniques, including graph databases [39], e.g., RDF (Resource Description Framework) triplestores, and relational databases [40]. On the one hand, vertical partitioning in relational database tables to store different sets of data properties [41] is subject to data linkage attacks as long as tables must be subject to relational joins to implement the mashup queries. On the other hand, querying data stored as vertically partitioned graph-based databases [42] can be serialized onto large tables and exposed to privacy concerns. The mashup of data sources in the Web of Data does not only affect distinct data sources from independent RDF triplestores containing resources that can be linked. Even a single-triplestore implementation of the Linked Data Platform (LDP) specification [43] might also require mashing up resources from different containers before running a query because some LDP sources vertically partition their resources into smaller containers, such that each resource is created within an instance of one of these container-like entities. Although containers are not normative in the LDP 1.0 specification, container-based implementations can also be exposed to adversarial attacks affecting data linking from vertically partitioned containers. Privacy-preserving data mashups on vertically partitioned datasets have been heavily focused on data mining, such as association rule mining [36; 44; 45], classification mining [46; 47; 48], or clustering [49; 50; 51]. For example, ref. [36] collaboratively computes association rule mining on vertically partitioned data to find common patterns.