Title: Application of differential privacy technology in multi-modal data sharing

Authors: Zhihai Lu; Bin Wang; Nuanqing Ouyang

Addresses: Information Center, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, Guangdong, China ' Information Center, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, Guangdong, China ' Library, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, Guangdong, China

Abstract: The advent of data-driven technologies and artificial intelligence (AI) has led to an increasing demand for the sharing and analysing sensitive information. However, the paramount concern of preserving individual privacy poses a significant challenge. Hence, an algorithm named differential privacy in data sharing for AI (PrivShareAI) has been utilised. The objective is to enable secure and privacy-preserving data sharing in AI by implementing differential privacy measures and maintaining a balance between utility and privacy. The data-sharing paradigm uses sensitivity limits, noise-enhanced queries, and a universal, secure architecture enabled by a trusted server to encourage shared learning while maintaining maximum privacy. The proposed model's efficiency is evaluated with baseline comparison studies with the following metrics: privacy guarantee, accuracy on varying ε parameters, privacy-utility trade-off, and privacy loss and accuracy measure.

Keywords: artificial intelligence; differential privacy; data sharing; accuracy; Gaussian noise; gradients; privacy guarantee.

DOI: 10.1504/IJIIDS.2025.147414

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.3/4, pp.340 - 360

Received: 16 Jan 2024
Accepted: 17 Jun 2024
Published online: 15 Jul 2025 *

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