In modern healthcare systems, data sources are highly integrated, and the privacy challenges are becoming a paramount concern. Despite the critical importance of privacy preservation in safeguarding sensitive and private information across various domains, there is a notable deficiency of learning and training material for privacy preservation. In this research, we present a k-anonymity algorithm explicitly for educational purposes. The development of the k-anonymity algorithm is complemented by seven validation tests, that have also been used as a basis for constructing five learning scenarios on privacy preservation. The outcomes of this research provide a practical understanding of a well-known privacy preservation technique and extends the familiarity of k-anonymity and the fundamental concepts of privacy protection to a broader audience.

Capture the flag (CTF) challenges are broadly used for engaging trainees in the technical aspects of cybersecurity, maintaining hands-on lab exercises, and integrating gamification elements. However, deploying the appropriate digital environment for conducting cybersecurity exercises can be challenging and typically requires a lot of effort and system resources by educators. In this paper, we present PocketCTF, an extensible and fully independent CTF platform, open to educators to run realistic virtual labs to host cybersecurity exercises in their classrooms. PocketCTF is based on containerization technologies to minimize the deployment effort and to utilize less system resources. A proof-of-concept implementation demonstrates the feasibility of deploying CTF challenges that allows the trainees to engage not only in offensive security but also in defensive tasks that have to be conducted during cybersecurity incidents. When using PocketCTF, educators can deploy hands-on labs, spending less time on the deployment and without necessarily having the advanced technical background to deploy complex labs and scenarios.

The Internet of Medical Things (IoMT) is a growing trend that has led to the use of connected devices, known as the Internet of Health. The healthcare domain has been a target of cyberattacks, especially with a large number of IoMT devices connected to hospital networks. This factor could allow attackers to access patients’ personal health information (PHI). This research paper proposes Chidroid, an innovative mobile Android application that can retrieve, collect, and distribute logs from smart healthcare devices. The proposed approach enables the creation of datasets, allowing non-structured data to be parsed into semi-structured or structured data that can be used for machine learning and deep learning, and the proposed approach can serve as a universal policy-based tool to examine and analyse security issues in most recent Android versions by distributing logs for analysis. The validation tests demonstrated that the application could retrieve logs and system metrics from various assets and devices in an efficient manner. The collected logs can provide visibility into the device’s activities and help to detect and mitigate potential security risks. This research introduces a way to perform a security analysis on Android devices that uses minimal system resources and reduces battery consumption by pushing the analysis stage to the edge.