"I Never Thought about Securing my Machine Learning Systems": A Study of Security and Privacy Awareness of Machine Learning Practitioners

AuthorBoenisch, Franziska; Battis, Verena; Buchmann, Nicolas; Poikela, Maija
TypeConference Paper
AbstractMachine learning (ML) models have become increasingly important components of many software systems. Therefore, ensuring their privacy and security is a crucial task. Current research mainly focuses on the development of security and privacy methods. However, ML practitioners, as the individuals in charge of translating the theory into practical applications, have not yet received much attention. In this paper, the security and privacy awareness and practices of ML practitioners are studied through an online survey with the aim of (1) gaining insight into the current state of awareness, (2) identifying influencing factors, and (3) exploring the actual use of existing methods and tools. The results indicate a relatively low general privacy and security awareness among the ML practitioners surveyed. In addition, they are less familiar with ML privacy protection methods than with general security methods or ML-related ones. Moreover, awareness correlates with the years of working with ML but not with the level of academic education or the field of occupation. Finally, the practitioners in this study seem to experience uncertainties in implementing legal frameworks, such as the European General Data Protection Regulation, into their ML workflows.
ConferenceKonferenz "Mensch und Computer" (MuC) 2021