Simone Fischer‐Hübner

Victor Morel, Leonardo Iwaya, Simone Fischer-Hübner

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.

Victor Morel, Simone Fischer-Hübner

Users are often overwhelmed by privacy decisions to manage their personal data, which can happen on the web, in mobile, and in IoT environments. These decisions can take various forms -- such as decisions for setting privacy permissions or privacy preferences, decisions responding to consent requests, or to intervene and ``reject'' processing of one's personal data --, and each can have different legal impacts. In all cases and for all types of decisions, scholars and industry have been proposing tools to better automate the process of privacy decisions at different levels, in order to enhance usability. We provide in this paper an overview of the main challenges raised by the automation of privacy decisions, together with a classification scheme of the existing and envisioned work and proposals addressing automation of privacy decisions.

Johanna Johansen, Tore Pedersen, Simone Fischer-Hübner et al.

Privacy is currently in distress and in need of rescue, much like princesses in the all-familiar fairytales. We employ storytelling and metaphors from fairytales to make reader-friendly and streamline our arguments about how a complex concept of Privacy Labeling (the 'knight in shining armor') can be a solution to the current state of Privacy (the 'princess in distress'). We give a precise definition of Privacy Labeling (PL), painting a panoptic portrait from seven different perspectives (the 'seven helpers'): Business, Legal, Regulatory, Usability and Human Factors, Educative, Technological, and Multidisciplinary. We describe a common vision, proposing several important 'traits of character' of PL as well as identifying 'undeveloped potentialities', i.e., open problems on which the community can focus. More specifically, this position paper identifies the stakeholders of the PL and their needs with regard to privacy, describing how PL should be and look like in order to address these needs. Throughout the paper, we highlight goals, characteristics, open problems, and starting points for creating, what we consider to be, the ideal PL. In the end we present three approaches to establish and manage PL, through: self-evaluations, certifications, or community endeavors. Based on these, we sketch a roadmap for future developments.