Jingwen Yan (Clemson University), Mohammed Aldeen (Clemson University), Jalil Harris (Clemson University), Kellen Grossenbacher (Clemson University), Aurore Munyaneza (Texas Tech University), Song Liao (Texas Tech University), Long Cheng (Clemson University)

As the number of mobile applications continues to grow, privacy labels (e.g. Apple’s Privacy Labels and Google’s Data Safety Section) emerge as a potential solution to help users understand how apps collect, use and share their data. However, it remains unclear whether these labels actually enhance user understanding to build trust in app developers or influence their download decisions. In this paper, we investigate user perceptions of privacy labels through a comprehensive analysis of online discussions and a structured user study. We first collect and analyze Reddit posts related to privacy labels, and manually analyze the discussions to understand users’ concerns and suggestions. Our analysis reveals that users are skeptical of self-reported privacy labels provided by developers and they struggle to interpret the terminology used in the labels. Users also expressed a desire for clearer explanations about why specific data is collected and emphasized the importance of third-party verification to ensure the accuracy of privacy labels. To complement our Reddit analysis, we conducted a user study with 50 participants recruited via Amazon Mechanical Turk and Qualtrics. The study revealed that 76% of the participants indicated that privacy labels influence their app download decisions and the amount of data practice in the privacy label is the most significant factor.

View More Papers

Black-box Membership Inference Attacks against Fine-tuned Diffusion Models

Yan Pang (University of Virginia), Tianhao Wang (University of Virginia)

Read More

SongBsAb: A Dual Prevention Approach against Singing Voice Conversion...

Guangke Chen (Pengcheng Laboratory), Yedi Zhang (National University of Singapore), Fu Song (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Science; Nanjing Institute of Software Technology), Ting Wang (Stony Brook University), Xiaoning Du (Monash University), Yang Liu (Nanyang Technological University)

Read More

Evaluating Machine Learning-Based IoT Device Identification Models for Security...

Eman Maali (Imperial College London), Omar Alrawi (Georgia Institute of Technology), Julie McCann (Imperial College London)

Read More

Evaluating LLMs Towards Automated Assessment of Privacy Policy Understandability

Keika Mori (Deloitte Tohmatsu Cyber LLC, Waseda University), Daiki Ito (Deloitte Tohmatsu Cyber LLC), Takumi Fukunaga (Deloitte Tohmatsu Cyber LLC), Takuya Watanabe (Deloitte Tohmatsu Cyber LLC), Yuta Takata (Deloitte Tohmatsu Cyber LLC), Masaki Kamizono (Deloitte Tohmatsu Cyber LLC), Tatsuya Mori (Waseda University, NICT, RIKEN AIP)

Read More