This paper explores the novel topic of data breach journalism and data breach news through the case of databreaches.net, a news outlet dedicated to data breaches and related cyber crime. Motivated by the issues in traditional crime news and crime journalism, the case is explored by the means of text mining. According to the results, the outlet has kept a steady publishing pace, mainly focusing on plain and short reporting but with generally high-quality source material for the news articles. Despite these characteristics, the news articles exhibit fairly strong sentiments, which is partially expected due to the presence of emotionally laden crime and the long history of sensationalism in crime news. The news site has also covered the full scope of data breaches, although many of these are fairly traditional, exposing personal identifiers and financial details of the victims. Also hospitals and the healthcare sector stand out. With these results, the paper advances the study of data breaches by considering these from the perspective of media and journalism.

Abstract Privacy policies are a mechanism used to inform users of the World Wide Web about the processing of their personal data. Such processing has special requirements, since personal data are regulated by data protection legislation. For example, a consent or another legal basis is typically needed. Privacy policies are documents used, among other things, to inform the data subject about processing of their personal data. These are formally represented by privacy languages. In this paper, we present a technique for constructing Layered Privacy Language policy data from web service code bases. Theoretically, we model the purposes of processing within web services by extending the privacy language with composition. We also present a formal analysis method for generating privacy policy purposes from the source code of web services. Furthermore, as a practical contribution, we present a static analysis tool that implements the theoretical solution. Finally, we report a brief case study for validating the tool

The General Data Protection Regulation (GDPR) came into force in 2018. After this enforcement, many fines have already been imposed by national data protection authorities in Europe. This paper examines the individual GDPR articles referenced in the enforcement decisions, as well as predicts the amount of enforcement fines with available meta-data and text mining features extracted from the enforcement decision documents. According to the results, three articles related to the general principles, lawfulness, and information security have been the most frequently referenced ones. Although the amount of fines imposed vary across the articles referenced, these three particular articles do not stand out. Furthermore, a better statistical evidence is available with other meta-data features, including information about the particular European countries in which the enforcements were made. Accurate predictions are attainable even with simple machine learning techniques for regression analysis. Basic text mining features outperform the meta-data features in this regard. In addition to these results, the paper reflects the GDPR’s enforcement against public administration obstacles in the European Union (EU), as well as discusses the use of automatic decision-making systems in judiciary.

Different security issues are a common problem for open source packages archived to and delivered through software ecosystems. These often manifest themselves as software weaknesses that may lead to concrete software vulnerabilities. This paper examines various security issues in Python packages with static analysis. The dataset is based on a snapshot of all packages stored to the Python Package Index (PyPI). In total, over 197 thousand packages and over 749 thousand security issues are covered. Even under the constraints imposed by static analysis, (a) the results indicate prevalence of security issues; at least one issue is present for about 46% of the Python packages. In terms of the issue types, (b) exception handling and different code injections have been the most common issues. The subprocess module stands out in this regard. Reflecting the generally small size of the packages, (c) software size metrics do not predict well the amount of issues revealed through static analysis. With these results and the accompanying discussion, the paper contributes to the field of large-scale empirical studies for better understanding security problems in software ecosystems.

The General Data Protection Regulation (GDPR) in the European Union is the most famous recently enacted privacy regulation. Despite of the regulation's legal, political, and technological ramifications, relatively little research has been carried out for better understanding the GDPR's practical implications for requirements engineering and software architectures. Building on a grounded theory approach with close ties to the Finnish software industry, this paper contributes to the sealing of this gap in previous research. Three questions are asked and answered in the context of software development organizations. First, the paper elaborates nine practical constraints under which many small and medium-sized enterprises (SMEs) often operate when implementing solutions that address the new regulatory demands. Second, the paper elicits nine regulatory requirements from the GDPR for software architectures. Third, the paper presents an implementation for a software architecture that complies both with the requirements elicited and the constraints elaborated.