Navigating Privacy and Copyright Challenges Across the Data Lifecycle of
Generative AI
- URL: http://arxiv.org/abs/2311.18252v2
- Date: Thu, 11 Jan 2024 01:53:48 GMT
- Title: Navigating Privacy and Copyright Challenges Across the Data Lifecycle of
Generative AI
- 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: 30.05760947688919
- License: http://creativecommons.org/licenses/by/4.0/
- 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
- 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) - 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) - Progress in Privacy Protection: A Review of Privacy Preserving
Techniques in Recommender Systems, Edge Computing, and Cloud Computing [2.9158689853305693]
This survey focuses on the areas of mobile crowdsourcing, edge computing, and recommender systems.
It explores the latest trends in these interconnected areas, with a special emphasis on privacy and data security.
arXiv Detail & Related papers (2024-01-20T19:32:56Z) - 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) - On Responsible Machine Learning Datasets with Fairness, Privacy, and
Regulatory Norms [58.93352076927003]
There have been severe concerns over the trustworthiness of AI technologies.
Machine and deep learning algorithms depend heavily on the data used during their development.
We propose a framework to evaluate the datasets through a responsible rubric.
arXiv Detail & Related papers (2023-10-24T14:01:53Z) - 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) - Security and Privacy on Generative Data in AIGC: A Survey [17.456578314457612]
We review the security and privacy on generative data in AIGC.
We reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance.
arXiv Detail & Related papers (2023-09-18T02:35:24Z) - A vision for global privacy bridges: Technical and legal measures for
international data markets [77.34726150561087]
Despite data protection laws and an acknowledged right to privacy, trading personal information has become a business equated with "trading oil"
An open conflict is arising between business demands for data and a desire for privacy.
We propose and test a vision of a personal information market with privacy.
arXiv Detail & Related papers (2020-05-13T13:55:50Z)
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.