# Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
## 6 Discussion  
Internet information systems and applications often use personal information, thus requiring a conservative treatment of PII and confidential information. Our PPVD mashup protocol has implications in the design and construction of information systems on the Internet.  
The privacy-by-design challenge is being tackled in the Web of Data by putting individuals in control of their own data through Personal Data Ecosystems based on SOLID principles [65]. In SOLID, individuals store their data on the Web as personal data stores or _pods_, such that each user has one or more pod from different web providers. Applications can access users' data using decentralized authentication and access control mechanisms to guarantee the privacy of the data. Web protocols and access control mechanisms do not sufficiently ensure users' data privacy as long as an adversary can mash up data from several pods and run data linkage attacks.  
Figure 4: (**a**) Data partition sent by the provider \(P_{a}\) during the confidential data collection (step 9 of the protocol). (**b**) Data partition sent by the provider \(P_{b}\) during the confidential data collection (step 9 of the protocol). Quasi-identifier attributes are marked in bold. The asterisk identifies the masked attributes.  
The Web of Things is also a key driver to understand the paradigm shift in e-learning towards context-aware, ubiquitous learning [66]. Internet-of-Things (IoT) technologies are convenient data gathering systems to build cooperative information systems on the Internet with different purposes, including e-learning. _Things_, such as devices enabled with computational and data storage capabilities, lay the foundations of cloud, fog, and edge computing [67] as the most recent trends in IoT-distributed computing. All such approaches have in common: the system data are spread over multiple devices and must be mashed up in a central point before making computer-aided data-informed decisions. IoT-based information systems, however, also pose a challenge to personal data privacy [68]. The paradigm shift of smart devices connected to the Internet requires considering data mashups in the Web of Things [69]. _Things_ are more prone to security risks because digital users' privacy is a fundamental right [70]. Among all security and privacy issues [71], the mashup of sparsely distributed data in the Web of Things is vulnerable to data linkage attacks by semi-honest intermediate entities part of the cloud, fog, or edge computing network infrastructure [72].  
Most related works propose preemptive access control [29] and authentication schemes [30] for data mashup privacy preservation in fog computing environments. However, the utility of the published data is lesser in such approaches because they are limited by design to expose publicly available data only. Instead, our protocol can publish all data attributes in a data mashup required for statistical analyses. It makes it with the help of a PPDP method of choice, which is independent of the actual mashup strategy. In contrast, privacy-preserving data partitioning solutions used in fog computing environments are based on simple noise addition [31].