Learning to Rank Privacy Design Patterns: A Semantic Approach to Meeting Privacy Requirements

Abstract

[Context and Motivation] Privacy requirements engineering is a critical aspect of software design to ensure that user data is protected in accordance with both regulatory and privacy objectives. The privacy requirements identified through this process can be addressed using various privacy design patterns. [Question/Problem] Identifying and implementing the most suitable privacy design patterns poses a major challenge for developers. They need to meticulously examine a wide range of options, which makes it challenging to quickly and effectively choose and justify the best solutions. [Key Ideas/Results] To address this gap, we developed a machine learning model that focuses on semantic text features and learning-to-rank algorithms to recommend privacy design patterns that meet specified privacy requirements. [Contribution] The main contribution of this paper is the development of a recommendation system for privacy design patterns based on privacy requirements using only text-based attributes. Our system’s reliance on text as the sole input guarantees its broad applicability, avoiding the constraints of fixed mappings prevalent in previous methodologies. The performance of the model has shown encouraging results in understanding the semantic meaning of privacy requirements and mapping them to privacy design patterns, indicating its suitability for inclusion in the privacy engineering process.