# Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks

arXiv: https://arxiv.org/abs/2310.07879 · HTML: https://mlsanity.com/paper/2310.07879/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2023.

## Built on by (most cited first, 30 of 31)

- [How Far Are We From AGI: Are LLMs All We Need?](https://mlsanity.com/paper/2405.10313/) (2024)
- [Granular Privacy Control for Geolocation with Vision Language Models](https://mlsanity.com/paper/2407.04952/) (2024)
- [ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs](https://mlsanity.com/paper/2409.09586/) (2024)
- [Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generators](https://mlsanity.com/paper/2402.01708/) (2024)
- [AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression](https://mlsanity.com/paper/2504.18932/) (2025)
- [TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories](https://mlsanity.com/paper/2511.21322/) (2025)
- [MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents](https://mlsanity.com/paper/2605.09530/) (2026)
- [Helping Johnny Make Sense of Privacy Policies with LLMs](https://mlsanity.com/paper/2501.16033/) (2025)
- [Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI](https://mlsanity.com/paper/2412.15047/) (2024)
- [Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts](https://mlsanity.com/paper/2509.23525/) (2025)
- [Sound Check: Auditing Audio Datasets](https://mlsanity.com/paper/2410.13114/) (2024)
- [Data Defenses Against Large Language Models](https://mlsanity.com/paper/2410.13138/) (2024)
- ["We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe](https://mlsanity.com/paper/2510.00909/) (2025)
- [Privacy in Human-AI Romantic Relationships: Concerns, Boundaries, and Agency](https://mlsanity.com/paper/2601.16824/) (2026)
- [Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection](https://mlsanity.com/paper/2411.19715/) (2024)
- ["Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications](https://mlsanity.com/paper/2503.03927/) (2025)
- [Understanding Users' Privacy Reasoning and Behaviors During Chatbot Use to Support Meaningful Agency in Privacy](https://mlsanity.com/paper/2601.18125/) (2026)
- [Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?](https://mlsanity.com/paper/2602.05023/) (2026)
- [CAREBench: A Child-Safety Risk Benchmark for Language Models](https://mlsanity.com/paper/2606.29685/) (2026)
- [Deep Learning Under Siege: Identifying Security Vulnerabilities and Risk Mitigation Strategies](https://mlsanity.com/paper/2409.09517/) (2024)
- [Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure](https://mlsanity.com/paper/2409.19104/) (2024)
- [Who is Responsible When AI Fails? Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidents](https://mlsanity.com/paper/2504.01029/) (2025)
- [AI Security Map: Holistic Organization of AI Security Technologies and Impacts on Stakeholders](https://mlsanity.com/paper/2508.08583/) (2025)
- [A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Review](https://mlsanity.com/paper/2509.20418/) (2025)
- [Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency](https://mlsanity.com/paper/2602.07754/) (2026)
- [Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agents](https://mlsanity.com/paper/2603.19416/) (2026)
- [Promoting Critical Thinking With Domain-Specific Generative AI Provocations](https://mlsanity.com/paper/2603.19975/) (2026)
- [Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI](https://mlsanity.com/paper/2604.17270/) (2026)
- [The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products](https://mlsanity.com/paper/2606.15485/) (2026)
- [Visible to the Court: How AI Is (and Isn't) Litigated in U.S. Federal Court Opinions](https://mlsanity.com/paper/2607.23888/) (2026)

## Abstract (arXiv)

Privacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy of AI privacy risks by analyzing 321 documented AI privacy incidents. We codified how the unique capabilities and requirements of AI technologies described in those incidents generated new privacy risks, exacerbated known ones, or otherwise did not meaningfully alter the risk. We present 12 high-level privacy risks that AI technologies either newly created (e.g., exposure risks from deepfake pornography) or exacerbated (e.g., surveillance risks from collecting training data). One upshot of our work is that incorporating AI technologies into a product can alter the privacy risks it entails. Yet, current approaches to privacy-preserving AI/ML (e.g., federated learning, differential privacy, checklists) only address a subset of the privacy risks arising from the capabilities and data requirements of AI.