Application security is becoming increasingly prevalent during software and especially web application development. Consequently, countermeasures are continuously being discussed and built into applications, with the goal of reducing the risk that unauthorized code will be able to access, steal, modify, or delete sensitive data. In this paper we gauged the presence and atmosphere surrounding security-related discussions on GitHub, as mined from discussions around commits and pull requests. First, we found that securityrelated discussions account for approximately 10% of all discussions on GitHub. Second, we found that more negative emotions are expressed in security-related discussions than in other discussions. These findings confirm the importance of properly training developers to address security concerns in their applications as well as the need to test applications thoroughly for security vulnerabilities in order to reduce frustration and improve overall project atmosphere.

De-identification is the process of removing the associations between data and identifying elements of individual data subjects. Its main purpose is to allow use of data while preserving the privacy of individual data subjects. It is thus an enabler for compliance with legal regulations such as the EU's General Data Protection Regulation. While many de-identification methods exist, the required knowledge regarding technical implications of different de-identification methods is largely missing. In this paper, we present a data utility-driven benchmark for different de-identification methods. The proposed solution systematically compares de-identification methods while considering their nature, context and de-identified data set goal in order to provide a combination of methods that satisfies privacy requirements while minimizing losses of data utility. The benchmark is validated in a prototype implementation which is applied to a real life data set.

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