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

arXiv: https://arxiv.org/abs/2303.08956 · HTML: https://mlsanity.com/paper/2303.08956/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets](https://mlsanity.com/paper/2106.10328/) (2021)
- [The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation](https://mlsanity.com/paper/1802.07228/) (2018)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Harms from Increasingly Agentic Algorithmic Systems](https://mlsanity.com/paper/2302.10329/) (2023)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [The Gradient of Generative AI Release: Methods and Considerations](https://mlsanity.com/paper/2302.04844/) (2023)
- [Perfectly Secure Steganography Using Minimum Entropy Coupling](https://mlsanity.com/paper/2210.14889/) (2022)
- [Structured access: an emerging paradigm for safe AI deployment](https://mlsanity.com/paper/2201.05159/) (2022)
- [Regulatory Markets for AI Safety](https://mlsanity.com/paper/2001.00078/) (2019)
- [AdvParams: An Active DNN Intellectual Property Protection Technique via Adversarial Perturbation Based Parameter Encryption](https://mlsanity.com/paper/2105.13697/) (2021)
- [On the Horizon: Interactive and Compositional Deepfakes](https://mlsanity.com/paper/2209.01714/) (2022)

## Built on by (most cited first, 6 of 6)

- [Model evaluation for extreme risks](https://mlsanity.com/paper/2305.15324/) (2023)
- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024)
- [The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AI](https://mlsanity.com/paper/2410.04931/) (2024)
- [Open Problems in AI Incident Governance](https://mlsanity.com/paper/2607.05163/) (2026)
- [Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech](https://mlsanity.com/paper/2607.05165/) (2026)

## 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.