Privacy Engineering in the Wild: Understanding the Practitioners’ Mindset, Organizational Aspects, and Current Practices

Abstract

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.

On the Evaluation of Privacy Impact Assessment and Privacy Risk Assessment Methodologies: A Systematic Literature Review

“…Case studies are shown to be one of the most common methodologies for evaluation; nonetheless, other methodologies could be considered, such as using grounded theory in the context of socio-technical systems [96]. This need for further evaluation research has also been discussed in the broader area of privacy engineering [14], [97].…”

“…The scope of the validation or evaluation, as well as the methodology and techniques employed, was of particular interest. This inquiry was necessitated by prevailing criticisms, which suggest a deficiency in these methodologies, particularly in terms of their lack of a rigorous and systematic evaluation process [9], [14].…”

“…Common issues reported by practitioners are that PIAs are overly complicated and time-consuming [10], steps are generic and abstract [11], and determining privacy risks is seen as vague and dependent on the skills and experience of the person performing the assessment [12], often in shortage of historic data [13]. Such drawbacks can also be extended to PRA and PTM methods since they can act as sub-components of PIAs as well as for other privacy engineering techniques [14].…”

“…PIAs play a fundamental role in assessing and addressing privacy risks in the development of new projects or systems that process personal data [14], [25]. As mentioned in the Introduction, a full-fledged PIA can incorporate a Privacy Risk Assessment (PRA) or Privacy Threat Modeling as core components of the PIA process.…”

Modelagem das Áreas de Risco de Sistemas de Detecção de Intrusão para Cálculo de Métricas de Privacidade

“…Privacidade na Internet tem sido um assunto recorrente tanto na literatura científica quanto em literatura para o público geral, inclusive em mídias sociais online [Silva and Franc ¸a 2023]. Essa preocupac ¸ão aumentou nos últimos anos principalmente por conta de regulamentac ¸ões como a LGPD (Lei Geral de Protec ¸ão de Dados Pessoais) no Brasil e a GDPR (General Data Protection Regulation) na Europa [Iwaya et al 2023]. Outro motivo que tem aumentado as discussões em torno de privacidade na Internet é o aumento da utilizac ¸ão de técnicas de inteligência artificial, principalmente aprendizado de máquina, para classificac ¸ão de dados.…”