Privacy Impact Assessments (PIAs), also known as Data Protection Impact Assessments (DPIAs) under the EU GDPR, and Privacy Risk Assessments (PRAs) have emerged as prominent privacy engineering methodologies, aiding developers and data controllers to systematically identify privacy risk and assign appropriate controls. As part of such methodologies, the concept of privacy harms has been proposed as a valuable, well-structured taxonomy that contributes to the rationalization and justification of assessment decisions made by practitioners. While some PRA methodologies include privacy harms, the impact of these inclusions based on practitioners’ perspectives remains largely unexplored. Hence, this study investigates whether evaluating predefined privacy harm categories, i.e., physical, psychological, financial/economic, reputational, and societal harms, can improve PRA outcomes by exploring PIA/DPIA and PRA practitioners’ perspectives. Using semi-structured interviews, including a workable PRA exercise, opinions and perspectives on privacy harms were elicited and analyzed following a reflexive thematic analysis. In total, 17 privacy practitioners were interviewed, revealing a range of positive (e.g., informative, educational) and negative (e.g., misleading, too broad) opinions on evaluating privacy harm categories. Further results indicate a lack of a standardized definition of privacy harm. In addition, participants noted that privacy harms are highly context-dependent and vary based on the data subject; hence, resulting in difficulty quantifying. Nevertheless, privacy harms are a critical addition to PIA/DPIA and PRA methodologies, supporting more rationalized and justifiable decisions when assessing risk, severity, and implementing mitigating controls. Yet, some prioritization of harm categories is advisable to efficiently allocate time and resources for assessment. • PIAs are important for assessing and selecting appropriate measures. • Some PIAs and privacy risk assessment methods comprise privacy harm categories. • These categories are considered valuable and beneficial during assessments.

Abstract An increasing number of mental health services are now offered through mobile health (mHealth) systems, such as in mobile applications (apps). Although there is an unprecedented growth in the adoption of mental health services, partly due to the COVID-19 pandemic, concerns about data privacy risks due to security breaches are also increasing. Whilst some studies have analyzed mHealth apps from different angles, including security, there is relatively little evidence for data privacy issues that may exist in mHealth apps used for mental health services, whose recipients can be particularly vulnerable. This paper reports an empirical study aimed at systematically identifying and understanding data privacy incorporated in mental health apps. We analyzed 27 top-ranked mental health apps from Google Play Store. Our methodology enabled us to perform an in-depth privacy analysis of the apps, covering static and dynamic analysis, data sharing behaviour, server-side tests, privacy impact assessment requests, and privacy policy evaluation. Furthermore, we mapped the findings to the LINDDUN threat taxonomy, describing how threats manifest on the studied apps. The findings reveal important data privacy issues such as unnecessary permissions, insecure cryptography implementations, and leaks of personal data and credentials in logs and web requests. There is also a high risk of user profiling as the apps’ development do not provide foolproof mechanisms against linkability, detectability and identifiability. Data sharing among 3rd-parties and advertisers in the current apps’ ecosystem aggravates this situation. Based on the empirical findings of this study, we provide recommendations to be considered by different stakeholders of mHealth apps in general and apps developers in particular. We conclude that while developers ought to be more knowledgeable in considering and addressing privacy issues, users and health professionals can also play a role by demanding privacy-friendly apps.

Privacy engineering, as an emerging field of research and practice, comprises the technical capabilities and management processes needed to implement, deploy, and operate privacy features and controls in working systems. For that, software practitioners and other stakeholders in software companies need to work cooperatively toward building privacy-preserving businesses and engineering solutions. Significant research has been done to understand the software practitioners' perceptions of information privacy, but more emphasis should be given to the uptake of concrete privacy engineering components. This research delves into the software practitioners' perspectives and mindset, organisational aspects, and current practices on privacy and its engineering processes. A total of 30 practitioners from nine countries and backgrounds were interviewed, sharing their experiences and voicing their opinions on a broad range of privacy topics. The thematic analysis methodology was adopted to code the interview data qualitatively and construct a rich and nuanced thematic framework. As a result, we identified three critical interconnected themes that compose our thematic framework for privacy engineering “in the wild”: (1) personal privacy mindset and stance, categorised into practitioners' privacy knowledge, attitudes and behaviours; (2) organisational privacy aspects, such as decision-power and positive and negative examples of privacy climate; and, (3) privacy engineering practices, such as procedures and controls concretely used in the industry. Among the main findings, this study provides many insights about the state-of-the-practice of privacy engineering, pointing to a positive influence of privacy laws (e.g., EU General Data Protection Regulation) on practitioners' behaviours and organisations' cultures. Aspects such as organisational privacy culture and climate were also confirmed to have a powerful influence on the practitioners' privacy behaviours. A conducive environment for privacy engineering needs to be created, aligning the privacy values of practitioners and their organisations, with particular attention to the leaders and top management's commitment to privacy. Organisations can also facilitate education and awareness training for software practitioners on existing privacy engineering theories, methods and tools that have already been proven effective.

Background: Docker technology has been increasingly used among software developers in a multitude of projects. This growing interest is due to the fact that Docker technology supports a convenient process for creating and building containers, promoting close cooperation between developer and operations teams, and enabling continuous software delivery. As a fast-growing technology, it is important to identify the Docker-related topics that are most popular as well as existing challenges and difficulties that developers face.

Community Health Workers (CHWs) have been using Mobile Health Data Collection Systems (MDCSs) for supporting the delivery of primary healthcare and carrying out public health surveys, feeding national-level databases with families' personal data. Such systems are used for public surveillance and to manage sensitive data (i.e., health data), so addressing the privacy issues is crucial for successfully deploying MDCSs. In this paper we present a comprehensive privacy threat analysis for MDCSs, discuss the privacy challenges and provide recommendations that are specially useful to health managers and developers. We ground our analysis on a large-scale MDCS used for primary care (GeoHealth) and a well-known Privacy Impact Assessment (PIA) methodology. The threat analysis is based on a compilation of relevant privacy threats from the literature as well as brain-storming sessions with privacy and security experts. Among the main findings, we observe that existing MDCSs do not employ adequate controls for achieving transparency and interveinability. Thus, threatening fundamental privacy principles regarded as data quality, right to access and right to object. Furthermore, it is noticeable that although there has been significant research to deal with data security issues, the attention with privacy in its multiple dimensions is prominently lacking.

A central component of managing risks in cloud computing is to understand the nature of security threats. The relevance of security concerns are evidenced by the efforts from both the academic community and technological organizations such as NIST, ENISA and CSA, to investigate security threats and vulnerabilities related to cloud systems. Provisioning secure virtual networks (SVNs) in a multi-tenant environment is a fundamental aspect to ensure trust in public cloud systems and to encourage their adoption. However, comparing existing SVN-oriented solutions is a difficult task due to the lack of studies summarizing the main concerns of network virtualization and providing a comprehensive list of threats those solutions should cover. To address this issue, this paper presents a threat classification for cloud networking, describing threat categories and attack scenarios that should be taken into account when designing, comparing, or categorizing solutions. The classification is based o n the CSA threat report, building upon studies and surveys from the specialized literature to extend the CSA list of threats and to allow a more detailed analysis of cloud network virtualization issues.

Security is one of the most imperative requirements for the success of systems that deal with highly sensitive data, such as medical information. However, many existing mobile health solutions focused on collecting patients' data at their homes that do not include security among their main requirements. Aiming to tackle this issue, this paper presents SecourHealth, a lightweight security framework focused on highly sensitive data collection applications. SecourHealth provides many security services for both stored and in-transit data, displaying interesting features such as tolerance to lack of connectivity (a common issue when promoting health in remote locations) and the ability to protect data even if the device is lost/stolen or shared by different data collection agents. Together with the system's description and analysis, we also show how SecourHealth can be integrated into a real data collection solution currently deployed in the city of Sao Paulo, Brazil.