SoK: A Classification for AI-driven Personalized Privacy Assistants

2025-02-11

AI Summary

The field of AI-driven Personalized Privacy Assistants (AI-PPAs) lacks systematic scholarly synthesis, hindering theoretical advancement and practical design. Method: We conduct the first comprehensive decade-spanning (2013–2023) survey, employing a Systematization of Knowledge (SoK), bibliometric analysis, and cross-dimensional qualitative coding to rigorously select and analyze 39 high-quality studies from an initial corpus of 1,697 publications. Contribution/Results: We propose the first multidimensional unified taxonomy—covering system architecture, AI paradigms, data sources, decision types, and user control—and identify critical research gaps and methodological shortcomings (e.g., insufficient transparency, low reproducibility). We introduce the first benchmark for evaluating AI-PPA design quality, deliver a structured classification model, establish privacy-enhancing AI design principles, and outline a concrete research roadmap—thereby providing both foundational theory and actionable guidance for the AI-PPA community.

Technology Category

• Philosophy and Ethics of AI: Privacy & Security
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Privacy

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Privacy-enhancing technologies
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

To help users make privacy-related decisions, personalized privacy assistants based on AI technology have been developed in recent years. These AI-driven Personalized Privacy Assistants (AI-driven PPAs) can reap significant benefits for users, who may otherwise struggle to make decisions regarding their personal data in environments saturated with privacy-related decision requests. However, no study systematically inquired about the features of these AI-driven PPAs, their underlying technologies, or the accuracy of their decisions. To fill this gap, we present a Systematization of Knowledge (SoK) to map the existing solutions found in the scientific literature. We screened 1697 unique research papers over the last decade (2013-2023), constructing a classification from 39 included papers. As a result, this SoK reviews several aspects of existing research on AI-driven PPAs in terms of types of publications, contributions, methodological quality, and other quantitative insights. Furthermore, we provide a comprehensive classification for AI-driven PPAs, delving into their architectural choices, system contexts, types of AI used, data sources, types of decisions, and control over decisions, among other facets. Based on our SoK, we further underline the research gaps and challenges and formulate recommendations for the design and development of AI-driven PPAs as well as avenues for future research.

Problem

• Classify AI-driven Personalized Privacy Assistants
• Review existing research on AI-driven PPAs
• Identify research gaps and future directions

Innovation

• AI-driven Personalized Privacy Assistants
• Systematization of Knowledge approach
• Comprehensive classification methodology