Jacob Hopkins (Texas A&M University - Corpus Christi), Carlos Rubio-Medrano (Texas A&M University - Corpus Christi), Cori Faklaris (University of North Carolina at Charlotte)

Data is a critical resource for technologies such as Large Language Models (LLMs) that are driving significant economic gains. Due to its importance, many different organizations are collecting and analyzing as much data as possible to secure their growth and relevance, leading to non-trivial privacy risks. Among the areas with potential for increased privacy risks are voluntary data-sharing events, when individuals willingly exchange their personal data for some service or item. This often places them in positions where they have inadequate control over what data should be exchanged and how it should be used. To address this power imbalance, we aim to obtain, analyze, and dissect the many different behaviors and needs of both parties involved in such negotiations, namely, the data subjects, i.e., the individuals whose data is being exchanged, and the data requesters, i.e., those who want to acquire the data. As an initial step, we are developing a multi-stage user study to better understand the factors that govern the behavior of both data subjects and requesters while interacting in data exchange negotiations. In addition, we aim to identify the design elements that both parties require so that future privacy-enhancing technologies (PETs) prioritizing privacy negotiation algorithms can be further developed and deployed in practice.

View More Papers

EAGLEYE: Exposing Hidden Web Interfaces in IoT Devices via...

Hangtian Liu (Information Engineering University), Lei Zheng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Shuitao Gan (Laboratory for Advanced Computing and Intelligence Engineering), Chao Zhang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zicong Gao (Information Engineering University), Hongqi Zhang (Henan Key Laboratory of Information Security), Yishun Zeng (Institute for Network Sciences…

Read More

RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial...

Dzung Pham (University of Massachusetts Amherst), Shreyas Kulkarni (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

Read More

ERW-Radar: An Adaptive Detection System against Evasive Ransomware by...

Lingbo Zhao (Institute of Information Engineering, Chinese Academy of Sciences), Yuhui Zhang (Institute of Information Engineering, Chinese Academy of Sciences), Zhilu Wang (Institute of Information Engineering, Chinese Academy of Sciences), Fengkai Yuan (Institute of Information Engineering, CAS), Rui Hou (Institute of Information Engineering, Chinese Academy of Sciences)

Read More