Membership inference attacks have emerged as a significant privacy concern in the training of deep learning models, where attackers can infer whether a data point was part of the training set based on the model's outputs. To address this challenge, we propose a novel defense mechanism, AdaMixup. AdaMixup employs adaptive mixup techniques to enhance the model's robustness against membership inference attacks by dynamically adjusting the mixup strategy during training. This method not only improves the model's privacy protection but also maintains high performance. Experimental results across multiple datasets demonstrate that AdaMixup significantly reduces the risk of membership inference attacks while achieving a favorable trade-off between defensive efficiency and model accuracy. This research provides an effective solution for data privacy protection and lays the groundwork for future advancements in mixup training methods.

Defined as the capability of a machine to mimic intelligent human behavior, AI encompasses a broad range of technologies, including machine learning, natural language processing, computer vision, and robotics. Its applications are far-reaching, impacting diverse fields such as healthcare, finance, manufacturing, retail, education, and more. The proliferation of data, advancements in computational power, and the development of sophisticated algorithms have accelerated the adoption of AI across industries. Businesses are leveraging AI not only to automate repetitive tasks but also to gain insights from data, improve customer experiences, and innovate in product and service offerings. In this paper, we provide an in-depth analysis of AI applications in four key industries: healthcare, finance, manufacturing, and retail. For each sector, we will examine the types of AI technologies being used, the problems they aim to solve, the benefits they bring, and the challenges associated with their deployment. We will also explore future trends and the broader implications of AI adoption.