What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media
arXiv:2606.06784v1 Announce Type: cross Abstract: Public social media posts can reveal private information through weak cues scattered across text, images, or metadata. Such leakage is often cumulative and cross-post: cues that appear harmless in isolation may jointly expose a user's home, workplace, or routine. However, current research lacks a unified benchmark for user-level multimodal privacy leakage and an evaluation metric that captures exposure severity beyond binary accuracy. To address these gaps, we propose SopriBench, a synthetic benchmark guided by leakage patterns abstracted from
The proliferation of AI and advanced data analysis techniques makes it increasingly possible to infer private user information from publicly available social media data, necessitating new benchmarks.
This development highlights the growing vulnerability of personal privacy in the age of pervasive social media and AI, impacting individuals, regulatory bodies, and AI developers.
The introduction of SopriBench and an agentic framework creates a standardized method to quantify and evaluate user-level privacy leakage, moving beyond simple binary assessments.
- · Privacy researchers
- · Cybersecurity firms
- · Regulatory bodies
- · Social media users
- · Social media platforms relying on user data
- · Data brokers
Increased focus on privacy-preserving AI and data handling within social media platforms.
New legislation and industry standards regarding AI's ability to infer private information from public data.
A societal shift in understanding public data, leading to more cautious online behavior and potentially encrypted social environments.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI