The wide adoption of virtual learning environments such as Moodle in numerous universities illustrate the growing trend of e-learning development and diffusion. These e-learning environments alter the relationship between the students and academic knowledge and learning processes considerably stimulating the students' autonomy by making most of the course material freely available at any time while inducing a progressive reduction of physical student-teacher interactions with virtual ones. Recent advances, as proposed in the TeSLA project, even introduces an e-assessment environment. This entire virtual learning framework raises new concerns in terms of privacy, given that such environments are potentially able to track the students, profile their habits, and retrieve personal data. In this paper, we analyze the influence of conception paradigms of e-learning platforms on personal data protection, based on a classification of these platforms in two antagonistic approaches. We illustrate our analysis with a case study of the TeSLA project and examine how the design choices impact the efficiency and legal compliance of personal data protection means. We finally propose alternative designs that could lead to significant improvements in this matter.

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Today, many modern cities adopt online smart parking services as best practices. Citizens can easily access these services using their smartphones or the infotainment panels in their cars. These services’ primary objective is to give drivers the ability to quickly identify free parking slots, which should reduce parking time, save fuel, and relieve traffic in urban areas. However, the privacy offered by these services should be comparable to that of the standard paper-based parking solutions offered by parking ticket machines. On the other hand, a privacy-preserving smart parking service’s design may raise a number of issues, including how to prevent double or multiple uses of parking tickets, how to prevent user tracking and profiling, how to revoke malicious users, how to handle data statistics without violating users’ privacy, and how to comply with regulations like the General Data Protection Regulation (GDPR). In this article, we present multidisciplinary research on a comprehensive vehicle parking system that protects users’ privacy. The research includes a range of topics, from the examination of regulatory compliance to the design of privacy-preserving parking registration and vehicle parking services to the implementation of privacy-preserving parking data processing features for data analysts. We provide a security analysis of our concept as well as several experimental results.

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