Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective

• URL: http://arxiv.org/abs/2311.18252v3
• Date: Sun, 17 Nov 2024 12:09:49 GMT
• Title: Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective
• Authors: Dawen Zhang, Boming Xia, Yue Liu, Xiwei Xu, Thong Hoang, Zhenchang Xing, Mark Staples, Qinghua Lu, Liming Zhu,
• Abstract summary: We discuss the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI.
• Score: 28.968233485060654
• License:
• 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.

Related papers

• Ethical Challenges in Computer Vision: Ensuring Privacy and Mitigating Bias in Publicly Available Datasets [0.0]
  This paper aims to shed light on the ethical problems of creating and deploying computer vision tech. Computer vision has become a vital tool in many industries, including medical care, security systems, and trade.
  arXiv  Detail & Related papers  (2024-08-31T00:59:29Z)
• Unlearning Targeted Information via Single Layer Unlearning Gradient [15.374381635334897]
  Unauthorized privacy-related computation is a significant concern for society. The EU's General Protection Regulation includes a "right to be forgotten" We propose Single Layer Unlearning Gradient (SLUG) to unlearn targeted information by updating targeted layers of a model.
  arXiv  Detail & Related papers  (2024-07-16T15:52:36Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI [4.627725143147341]
  We study the concerns regarding the intellectual property rights of training data. We focus on the properties of generative models that enable misuse leading to potential IP violations.
  arXiv  Detail & Related papers  (2024-04-22T09:09:21Z)
• Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI [2.669847575321326]
  The survey aims to stay abreast of the latest developments and open problems. It will first outline methods of detecting copyright infringement in mediums such as text, image, and video. Next, it will delve an exploration of existing techniques aimed at safeguarding copyrighted works from generative models.
  arXiv  Detail & Related papers  (2024-03-31T22:10:01Z)
• Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
  We propose a hardware-level face de-identification method to solve this vulnerability. We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
  arXiv  Detail & Related papers  (2024-03-31T19:28:04Z)
• Copyright Protection in Generative AI: A Technical Perspective [58.84343394349887]
  Generative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity and authenticity of contents generated by these Deep Generative Models (DGMs) have sparked significant copyright concerns. This work delves into this issue by providing a comprehensive overview of copyright protection from a technical perspective.
  arXiv  Detail & Related papers  (2024-02-04T04:00:33Z)
• A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models [52.49582606341111]
  Copyright law confers creators the exclusive rights to reproduce, distribute, and monetize their creative works. Recent progress in text-to-image generation has introduced formidable challenges to copyright enforcement. We introduce a novel pipeline that harmonizes CLIP, ChatGPT, and diffusion models to curate a dataset.
  arXiv  Detail & Related papers  (2024-01-04T11:14:01Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Privacy and Robustness in Federated Learning: Attacks and Defenses [74.62641494122988]
  We conduct the first comprehensive survey on this topic. Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic.
  arXiv  Detail & Related papers  (2020-12-07T12:11:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.