Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929594128400384 |
|---|---|
| author | Zhang, Dawen Xia, Boming Liu, Yue Xu, Xiwei Hoang, Thong Xing, Zhenchang Staples, Mark Lu, Qinghua Zhu, Liming |
| author_facet | Zhang, Dawen Xia, Boming Liu, Yue Xu, Xiwei Hoang, Thong Xing, Zhenchang Staples, Mark Lu, Qinghua Zhu, Liming |
| contents | The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns. However, these advancements come with heightened concerns over data privacy and copyright infringement, primarily due to the reliance on vast datasets for model training. Traditional approaches like differential privacy, machine unlearning, and data poisoning only offer fragmented solutions to these complex issues. Our paper delves into the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight, holistically addressing these concerns by investigating and devising solutions that are informed by the lifecycle perspective. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18252 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective Zhang, Dawen Xia, Boming Liu, Yue Xu, Xiwei Hoang, Thong Xing, Zhenchang Staples, Mark Lu, Qinghua Zhu, Liming Software Engineering Artificial Intelligence Computers and Society Machine Learning The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns. However, these advancements come with heightened concerns over data privacy and copyright infringement, primarily due to the reliance on vast datasets for model training. Traditional approaches like differential privacy, machine unlearning, and data poisoning only offer fragmented solutions to these complex issues. Our paper delves into the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight, holistically addressing these concerns by investigating and devising solutions that are informed by the lifecycle perspective. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI. |
| title | Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective |
| topic | Software Engineering Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2311.18252 |