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

• URL: http://arxiv.org/abs/2609.07217v1
• Date: Mon, 07 Sep 2026 08:34:10 GMT
• Title: Enhancing Privacy, Neglecting Harms: An Analysis of Real-World Digital Privacy Incidents
• Abstract summary: We revisit our understanding of privacy incidents and the realities of those experiencing privacy harms.<n>We develop a new information flow model that encompasses the complexity of data flows and their relation to resulting harms.
• Score: 46.19630304097305
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Profiling for Pennies: Unveiling the Privacy Iceberg of LLM Agents [52.02752011119451]
  We investigate privacy perception in the real world through the lens of human awareness.<n>We propose the PrivacyIceberg, which categorizes real-world human privacy risks into three tiers.
  arXiv  Detail & Related papers  (2026-05-07T13:21:44Z)
• Neural Gate: Mitigating Privacy Risks in LVLMs via Neuron-Level Gradient Gating [71.55435880263238]
  We introduce Neural Gate, a novel method for mitigating privacy risks through neuron-level model editing.<n>Our method improves a model's privacy safeguards by increasing its rate of refusal for privacy-related questions.
  arXiv  Detail & Related papers  (2026-03-13T03:03:20Z)
• Adopt a PET! An Exploration of PETs, Policy, and Practicalities for Industry in Canada [2.634702601759193]
  Privacy enhancing technologies (PETs) are technical solutions for privacy issues.<n>PETs allow for the development of solutions that benefit society, all while ensuring the privacy of individuals whose data is being used.<n>Despite increased privacy challenges and a corresponding increase in new regulations being proposed by governments across the globe, a low adoption rate of PETs persists.
  arXiv  Detail & Related papers  (2025-03-04T22:08:56Z)
• Smoke Screens and Scapegoats: The Reality of General Data Protection Regulation Compliance -- Privacy and Ethics in the Case of Replika AI [1.325665193924634]
  This paper takes a critical approach towards examining the intricacies of these issues within AI companion services. We analyze articles from public media about the company and its practices to gain insight into the trustworthiness of information provided in the policy. The results reveal despite privacy notices, data collection practices might harvest personal data without users' full awareness.
  arXiv  Detail & Related papers  (2024-11-07T07:36:19Z)
• PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action [54.11479432110771]
  PrivacyLens is a novel framework designed to extend privacy-sensitive seeds into expressive vignettes and further into agent trajectories.<n>We instantiate PrivacyLens with a collection of privacy norms grounded in privacy literature and crowdsourced seeds.<n>State-of-the-art LMs, like GPT-4 and Llama-3-70B, leak sensitive information in 25.68% and 38.69% of cases, even when prompted with privacy-enhancing instructions.
  arXiv  Detail & Related papers  (2024-08-29T17:58:38Z)
• When PETs misbehave: A Contextual Integrity analysis [0.7397067779113841]
  We use the theory of Contextual Integrity to explain how privacy technologies may be misused to erode privacy. We consider three PETs and scenarios: anonymous credentials for age verification, client-side scanning for illegal content detection, and homomorphic encryption for machine learning model training.
  arXiv  Detail & Related papers  (2023-12-05T05:27:43Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Security and Privacy on Generative Data in AIGC: A Survey [17.456578314457612]
  We review the security and privacy on generative data in AIGC. We reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance.
  arXiv  Detail & Related papers  (2023-09-18T02:35:24Z)
• TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection [59.04634695294402]
  Video anomaly detection (VAD) without human monitoring is a complex computer vision task. Privacy leakage in VAD allows models to pick up and amplify unnecessary biases related to people's personal information. We propose TeD-SPAD, a privacy-aware video anomaly detection framework that destroys visual private information in a self-supervised manner.
  arXiv  Detail & Related papers  (2023-08-21T22:42:55Z)
• Digital Privacy Under Attack: Challenges and Enablers [11.061112334099597]
  We systematically categorize attacks targeting three domains: anonymous data, statistical aggregates, and privacy-preserving models.<n>For each category, we analyze attack methodologies, adversary capabilities, and vulnerability mechanisms.<n>Our analysis reveals that while differential privacy offers strong theoretical guarantees, it faces implementation challenges and potential vulnerabilities to emerging attacks.
  arXiv  Detail & Related papers  (2023-02-18T08:34:34Z)
• Privacy in Open Search: A Review of Challenges and Solutions [0.6445605125467572]
  Information retrieval (IR) is prone to privacy threats, such as attacks and unintended disclosures of documents and search history. This work aims at highlighting and discussing open challenges for privacy in the recent literature of IR, focusing on tasks featuring user-generated text data.
  arXiv  Detail & Related papers  (2021-10-20T18:38:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.