mlsanityData to 7 Oct 2026

Exploring the Relevance of Data Privacy-Enhancing Technologies for AI Governance Use Cases

arXiv 2303.08956 ↗First posted 15 Mar 2023cs.AI cs.CRcited by 5

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Abstract (arXiv)

The development of privacy-enhancing technologies has made immense progress in reducing trade-offs between privacy and performance in data exchange and analysis. Similar tools for structured transparency could be useful for AI governance by offering capabilities such as external scrutiny, auditing, and source verification. It is useful to view these different AI governance objectives as a system of information flows in order to avoid partial solutions and significant gaps in governance, as there may be significant overlap in the software stacks needed for the AI governance use cases mentioned in this text. When viewing the system as a whole, the importance of interoperability between these different AI governance solutions becomes clear. Therefore, it is imminently important to look at these problems in AI governance as a system, before these standards, auditing procedures, software, and norms settle into place.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 61 references that are in the map. First posted on arXiv 15 Mar 2023.

Builds on

16 of 61 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
  4. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
  5. 2021Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasetscited by 179
  6. 2018The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigationcited by 159
  7. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
  8. 2023Auditing large language models: a three-layered approachcited by 135
  9. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 134
  10. 2018A generic framework for privacy preserving deep learningcited by 103
  11. 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
  12. 2022Perfectly Secure Steganography Using Minimum Entropy Couplingcited by 42
4 more
  1. 2022Structured access: an emerging paradigm for safe AI deploymentcited by 40
  2. 2019Regulatory Markets for AI Safetycited by 13
  3. 2022On the Horizon: Interactive and Compositional Deepfakescited by 13
  4. 2021AdvParams: An Active DNN Intellectual Property Protection Technique via Adversarial Perturbation Based Parameter Encryptioncited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.

5 papers.

  1. 2023Model evaluation for extreme riskscited by 167
  2. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 123
  3. 2024Visibility into AI Agentscited by 77
  4. 2024The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AIcited by 6
  5. 2026Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speechcited by 0