# AI-driven Personalized Privacy Assistants: a Systematic Literature Review

arXiv: https://arxiv.org/abs/2502.07693 · HTML: https://mlsanity.com/paper/2502.07693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 90 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 11 of 11)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explainable Reinforcement Learning: A Survey](https://mlsanity.com/paper/2005.06247/) (2020)
- [Large Language Models: A New Approach for Privacy Policy Analysis at Scale](https://mlsanity.com/paper/2405.20900/) (2024)
- [GenAIPABench: A Benchmark for Generative AI-based Privacy Assistants](https://mlsanity.com/paper/2309.05138/) (2023)
- [Enhanced Security and Privacy via Fragmented Federated Learning](https://mlsanity.com/paper/2207.05978/) (2022)
- [Uncertainty-aware Personal Assistant for Making Personalized Privacy Decisions](https://mlsanity.com/paper/2205.06544/) (2022)
- [Predicting Privacy Preferences for Smart Devices as Norms](https://mlsanity.com/paper/2302.10650/) (2023)
- [PPM: A Privacy Prediction Model for Online Social Networks](https://mlsanity.com/paper/1606.07463/) (2016)
- [Automating privacy decisions -- where to draw the line?](https://mlsanity.com/paper/2305.08747/) (2023)
- [Privacy Engineering From Principles to Practice: A Roadmap](https://mlsanity.com/paper/2404.03442/) (2024)

## Abstract (arXiv)

In recent years, several personalized assistants based on AI have been researched and developed to help users make privacy-related decisions. These AI-driven Personalized Privacy Assistants (AI-driven PPAs) can provide significant benefits for users, who might otherwise struggle with making decisions about their personal data in online environments that often overload them with different privacy decision requests. So far, no studies have systematically investigated the emerging topic of AI-driven PPAs, classifying their underlying technologies, architecture and features, including decision types or the accuracy of their decisions. To fill this gap, we present a Systematic Literature Review (SLR) to map the existing solutions found in the scientific literature, which allows reasoning about existing approaches and open challenges for this research field. We screened several hundred unique research papers over the recent years (2013-2025), constructing a classification from 41 included papers. As a result, this SLR 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 SLR, 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.