Article: Compliance challenges in AI training data usage: a novel mechanism for fusion generative adversarial networks Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.31 pp.62 - 79 Abstract: The rapid advancement of artificial intelligence faces challenges in training data copyright and privacy compliance. Existing techniques often struggle to balance the quality and security of synthetic data, leading to a dilemma where solutions are either low in utility or high in risk. To address this, this article proposes a novel generative adversarial network incorporating a compliance-aware mechanism. This framework introduces a specialised compliance discriminator to guide the model in generating synthetic data that is both highly realistic and strictly compliant. Experiments on public datasets demonstrate that our method maintains classification accuracy within 0.8% of the original data while significantly reducing sensitive information leakage risk by 42%. Statistical validation confirms that all key metric improvements are statistically significant. This work provides an effective approach to resolving the trade-off between data compliance and utility. Inderscience Publishers - linking academia, business and industry through research

Title: Compliance challenges in AI training data usage: a novel mechanism for fusion generative adversarial networks

Authors: Muling Yuan

Addresses: School of Law, Guangzhou College of Commerce, Guangzhou, 510000, China

Abstract: The rapid advancement of artificial intelligence faces challenges in training data copyright and privacy compliance. Existing techniques often struggle to balance the quality and security of synthetic data, leading to a dilemma where solutions are either low in utility or high in risk. To address this, this article proposes a novel generative adversarial network incorporating a compliance-aware mechanism. This framework introduces a specialised compliance discriminator to guide the model in generating synthetic data that is both highly realistic and strictly compliant. Experiments on public datasets demonstrate that our method maintains classification accuracy within 0.8% of the original data while significantly reducing sensitive information leakage risk by 42%. Statistical validation confirms that all key metric improvements are statistically significant. This work provides an effective approach to resolving the trade-off between data compliance and utility.

Keywords: data compliance; generative adversarial networks; synthetic data; privacy protection; adversarial training.

DOI: 10.1504/IJICT.2026.152855

International Journal of Information and Communication Technology, 2026 Vol.27 No.31, pp.62 - 79

Received: 03 Dec 2025
Accepted: 01 Jan 2026
Published online: 13 Apr 2026 *

Title: Compliance challenges in AI training data usage: a novel mechanism for fusion generative adversarial networks

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