Leona Lassak (Ruhr University Bochum), Hanna Püschel (TU Dortmund University), Oliver D. Reithmaier (Leibniz University Hannover), Tobias Gostomzyk (TU Dortmund University), Markus Dürmuth (Leibniz University Hannover)

In times of big data, connected devices, and increasing self-measurement, protecting consumer privacy remains a challenge despite ongoing technological and legislative efforts. Data trustees present a promising solution, aiming to balance data utilization with privacy concerns by facilitating secure data sharing and ensuring individual control. However, successful implementation hinges on user acceptance and trust.

We conducted a large-scale, vignette-based, census-representative online study examining factors influencing the acceptance of data trustees for medical, automotive, IoT, and online data. With n=714 participants from Germany and n=1036 from the US, our study reveals varied willingness to use data trustees across both countries, with notable skepticism and outright rejection from a significant portion of users.

We also identified significant domain-specific differences, including the influence of user anonymity, perceived personal and societal benefits, and the recipients of the data.

Contrary to common beliefs, organizational and regulatory decisions such as the storage location, the operator, and supervision appeared less relevant to users' decisions.

In conclusion, while there exists a potential user base for data trustees, achieving widespread acceptance will require explicit and targeted implementation strategies tailored to address diverse user expectations. Our findings underscore the importance of understanding these nuances for effectively deploying data trustee frameworks that meet both regulatory requirements and user preferences while upholding highest security and privacy standards.

View More Papers

“Do We Call Them That? Absolutely Not.”: Juxtaposing the...

Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Luca Favaro (Technical University of Munich), and Florian Matthes (Technical University of Munich)

Read More

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian...

Kaiyuan Zhang (Purdue University), Siyuan Cheng (Purdue University), Guangyu Shen (Purdue University), Bruno Ribeiro (Purdue University), Shengwei An (Purdue University), Pin-Yu Chen (IBM Research AI), Xiangyu Zhang (Purdue University), Ninghui Li (Purdue University)

Read More

Be Careful of What You Embed: Demystifying OLE Vulnerabilities

Yunpeng Tian (Huazhong University of Science and Technology), Feng Dong (Huazhong University of Science and Technology), Haoyi Liu (Huazhong University of Science and Technology), Meng Xu (University of Waterloo), Zhiniang Peng (Huazhong University of Science and Technology; Sangfor Technologies Inc.), Zesen Ye (Sangfor Technologies Inc.), Shenghui Li (Huazhong University of Science and Technology), Xiapu Luo…

Read More

The Midas Touch: Triggering the Capability of LLMs for...

Yi Yang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Jinghua Liu (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Kai Chen (Institute of Information Engineering, Chinese Academy of…

Read More