User Acceptance Criteria for Privacy Preserving Machine Learning Techniques

User Acceptance Criteria for Privacy Preserving Machine Learning Techniques

연구 분야: Verification

학회: ARES '23: Proceedings of the 18th International Conference on Availability, Reliability and Security

Users are confronted with a variety of different machine learning applications in many domains. To make this possible especially for applications relying on sensitive data, companies and developers are implementing Privacy Preserving Machine Learning (PPML) techniques what is already a challenge in itself. This study provides the first step for answering the question how to include the user’s preferences for a PPML technique into the privacy by design process, when developing a new application. The goal is to support developers and AI service providers when choosing a PPML technique that best reflects the users’ preferences. Based on discussions with privacy and PPML experts, we derived a framework that maps the characteristics of PPML to user acceptance criteria.