Enhancing Privacy, Neglecting Harms: An Analysis of Real-World Digital Privacy Incidents

2026-09-07

AI Summary

本文通过分析257起真实世界中的数字隐私事件，提出了一种新的信息流模型来解释为何现有隐私增强技术未能有效防止隐私伤害，并指出需要转变当前隐私技术研究的方向。

Abstract

Privacy-enhancing technologies (PETs) have emerged as a technical means for providing individuals with greater control over their information. Yet despite the growing deployment of PETs, people continue to experience privacy harms. In this work, we revisit our understanding of privacy incidents and the realities of those experiencing privacy harms, to assess whether the goals and abilities of PETs are misaligned with the harms people face. For our study, we collect news articles that correspond to a sample of 257 real-world privacy incidents. We employ content analysis over the articles to develop a new information flow model that encompasses the complexity of data flows and their relation to resulting harms. We demonstrate that our model captures both established and novel aspects of privacy incidents and their mitigations. In particular, it captures why consent is often insufficient to prevent privacy violations, how harms emerge from complex interactions among multiple entities and actions, and reveals a flaw in our understanding of PETs: a focus on enabling functionalities still permits the harms inherent in those functionalities. Moreover, we find that the entities best positioned to implement harm-preventing measures for the incidents in our sample are the least incentivized to do so. Overall, our model and analysis identify limitations of privacy technology research for harm prevention and further identifies paths for transforming how we approach the advancement of these technologies.

Problem

• Privacy-enhancing technologies
• privacy harms
• information flow model

Innovation

• Privacy-enhancing technologies
• information flow model
• privacy harms