# Can Copyright be Reduced to Privacy?

arXiv: https://arxiv.org/abs/2305.14822 · HTML: https://mlsanity.com/paper/2305.14822/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 65 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 10 of 10)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Reconstructing Training Data from Trained Neural Networks](https://mlsanity.com/paper/2206.07758/) (2022)
- [Foundation Models and Fair Use](https://mlsanity.com/paper/2303.15715/) (2023)
- [On Provable Copyright Protection for Generative Models](https://mlsanity.com/paper/2302.10870/) (2023)
- [Selective Differential Privacy for Language Modeling](https://mlsanity.com/paper/2108.12944/) (2021)
- [Privacy-preserving Prediction](https://mlsanity.com/paper/1803.10266/) (2018)
- [Simultaneous Private Learning of Multiple Concepts](https://mlsanity.com/paper/1511.08552/) (2015)
- [Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity](https://mlsanity.com/paper/2206.01230/) (2022)
- [DeepCreativity: Measuring Creativity with Deep Learning Techniques](https://mlsanity.com/paper/2201.06118/) (2022)

## Built on by (most cited first, 24 of 24)

- [DE-COP: Detecting Copyrighted Content in Language Models Training Data](https://mlsanity.com/paper/2402.09910/) (2024)
- [SoK: Memorization in General-Purpose Large Language Models](https://mlsanity.com/paper/2310.18362/) (2023)
- [Automatic Jailbreaking of the Text-to-Image Generative AI Systems](https://mlsanity.com/paper/2405.16567/) (2024)
- [Data Attribution for Text-to-Image Models by Unlearning Synthesized Images](https://mlsanity.com/paper/2406.09408/) (2024)
- [On the Edge of Memorization in Diffusion Models](https://mlsanity.com/paper/2508.17689/) (2025)
- [Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?](https://mlsanity.com/paper/2407.17417/) (2024)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes](https://mlsanity.com/paper/2403.17691/) (2024)
- [Copyright-Protected Language Generation via Adaptive Model Fusion](https://mlsanity.com/paper/2412.06619/) (2024)
- [Fast Data Attribution for Text-to-Image Models](https://mlsanity.com/paper/2511.10721/) (2025)
- [VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models](https://mlsanity.com/paper/2312.00057/) (2023)
- [Strong Copyright Protection for Language Models via Adaptive Model Fusion](https://mlsanity.com/paper/2407.20105/) (2024)
- [Not Every Image is Worth a Thousand Words: Quantifying Originality in Stable Diffusion](https://mlsanity.com/paper/2408.08184/) (2024)
- [RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model](https://mlsanity.com/paper/2408.16634/) (2024)
- [Credit Attribution and Stable Compression](https://mlsanity.com/paper/2406.15916/) (2024)
- [Probabilistic Analysis of Copyright Disputes and Generative AI Safety](https://mlsanity.com/paper/2410.00475/) (2024)
- [A Lightweight Method to Disrupt Memorized Sequences in LLM](https://mlsanity.com/paper/2502.05159/) (2025)
- [Randomization Techniques to Mitigate the Risk of Copyright Infringement](https://mlsanity.com/paper/2408.13278/) (2024)
- [Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture](https://mlsanity.com/paper/2410.04454/) (2024)
- [Interrogating LLM design under a fair learning doctrine](https://mlsanity.com/paper/2502.16290/) (2025)
- [Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models](https://mlsanity.com/paper/2506.19881/) (2025)
- [Reconstructing Template-Memorized Images from Natural Prompts](https://mlsanity.com/paper/2507.07947/) (2025)
- [Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings](https://mlsanity.com/paper/2411.14639/) (2024)
- [We Should Separate Memorization from Copyright](https://mlsanity.com/paper/2602.08632/) (2026)

## Abstract (arXiv)

There is a growing concern that generative AI models will generate outputs closely resembling the copyrighted materials for which they are trained. This worry has intensified as the quality and complexity of generative models have immensely improved, and the availability of extensive datasets containing copyrighted material has expanded. Researchers are actively exploring strategies to mitigate the risk of generating infringing samples, with a recent line of work suggesting to employ techniques such as differential privacy and other forms of algorithmic stability to provide guarantees on the lack of infringing copying. In this work, we examine whether such algorithmic stability techniques are suitable to ensure the responsible use of generative models without inadvertently violating copyright laws. We argue that while these techniques aim to verify the presence of identifiable information in datasets, thus being privacy-oriented, copyright law aims to promote the use of original works for the benefit of society as a whole, provided that no unlicensed use of protected expression occurred. These fundamental differences between privacy and copyright must not be overlooked. In particular, we demonstrate that while algorithmic stability may be perceived as a practical tool to detect copying, such copying does not necessarily constitute copyright infringement. Therefore, if adopted as a standard for detecting an establishing copyright infringement, algorithmic stability may undermine the intended objectives of copyright law.