Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective

Dawen Zhang, Boming Xia, Yue Liu, Xiwei Xu, Thong Hoang +4 more
11/30/2023
cs.SEcs.AIcs.CY

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Abstract

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.

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Cite this paper

@article{zhang2023privacy,
  title  = {Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective},
  author = {Dawen Zhang and Boming Xia and Yue Liu and Xiwei Xu and Thong Hoang and Zhenchang Xing and Mark Staples and Qinghua Lu and Liming Zhu},
  year   = {2023},
  eprint = {2311.18252},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.18252v3}
}

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