Bystander Privacy in Video Sharing Era: Automated Consent Compliance through Platform Censorship

要旨

Bystander privacy has become a critical concern amidst the widespread activities of video sharing, engaging billions of users daily. Concerns arise when individuals inadvertently appear in public videos without consent. Existing methods for determining bystander permissions require significant adaptation and modifications by videographers and video sharing platforms, potentially limiting their adoption. This study explores leveraging platform censorship capabilities to enforce bystander privacy. We introduce selfFlag, a type of violative media signal designed to trigger automatic content flagging. Bystanders exhibiting such signals, captured in public videos, can be automatically identified and removed by platforms, thereby indirectly enforcing privacy preferences, primarily through the efforts of bystanders themselves. We conduct thorough measurements on current censorship practices, propose music-based triggering content, and develop an auxiliary tool for videographers to produce high-quality content with privacy compliance.

著者
Si LIAO
ShanghaiTech University, Shanghai, China
Hanwei He
ShanghaiTech University, Shanghai, China
Huangxun Chen
The Hong Kong Univeristy of Science and Technology (Guangzhou), Guangzhou , Guangdong, China
Zhice Yang
ShanghaiTech University, Shanghai, China
DOI

10.1145/3706598.3713391

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713391

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Risk and Privacy

G302
6 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
日本語まとめ
読み込み中…