With the advancement of data-intensive technologies and online tracking opportunities, education on privacy is becoming a bigger priority in cybersecurity curricula. Owing to its multidimensional and context-dependent nature, privacy and its protection goals can be introduced to computer science students as collective efforts of system designers to enable data protection, user's informational control, and privacy-preserving data sharing with third parties. Since engaging course participants into hands-on and gamified exercises is generally known to enhance learning experience and effect, we adopted the teaching practice of using Capture-the-Flag (CTF) security challenges and validated its applicability in the privacy education domain. This work presents a pioneering set of 8 CTF-style tasks intentionally designed for the study and demonstration of handpicked privacy concepts, techniques, and attacks. Drawing upon both quantitative and qualitative feedback collected from 27 course participants, this format of homework exercises is found to increase students' confidence in this knowledge domain and perceived as an enjoyable, motivating, and engaging way of learning about information privacy.

Crypto-assets are unique in tying financial wealth to the secrecy of private keys. Prior empirical work has attempted to study end-user security from both technical and organizational perspectives. However, the link between individuals’ risk perceptions and security behavior was often obscured by the heterogeneity of the subjects in small samples. This paper contributes quantitative results from a survey of 395 crypto-asset users recruited by a novel combination of deep and broad sampling. The analysis accounts for heterogeneity with a new typology that partitions the sample in three robust clusters – cypherpunks, hodlers, and rookies – using five psychometric constructs. The constructs originate from established behavioral theories with items purposefully adapted to the domain. We demonstrate the utility of this typology in better understanding users’ characteristics and security behaviors. These insights inform the design of crypto-asset solutions, guide risk communication, and suggest directions for future digital currencies.