Machine Learning (ML) models integrated with in-situ sensing offer transformative solutions for defect detection in Additive Manufacturing (AM), but this integration brings critical challenges in safeguarding sensitive data, such as part designs and material compositions. Differential Privacy (DP), which introduces mathematically controlled noise, provides a balance between data utility and privacy. However, black-box Artificial Intelligence (AI) models often obscure how this noise impacts model accuracy, complicating the optimization of privacy-accuracy trade-offs. This study introduces the Differential Privacy-Hyperdimensional Computing (DP-HD) framework, a novel approach combining Explainable AI (XAI) and vector symbolic paradigms to quantify and predict noise effects on accuracy using a Signal-to-Noise Ratio (SNR) metric. DP-HD enables precise tuning of DP noise levels, ensuring an optimal balance between privacy and performance. The framework has been validated using real-world AM data, demonstrating its applicability to industrial environments. Experimental results demonstrate DP-HD's capability to achieve state-of-the-art accuracy (94.43%) with robust privacy protections in anomaly detection for AM, even under significant noise conditions. Beyond AM, DP-HD holds substantial promise for broader applications in privacy-sensitive domains such as healthcare, financial services, and government data management, where securing sensitive data while maintaining high ML performance is paramount.

翻译：与现场传感集成的机器学习模型为增材制造缺陷检测提供了变革性解决方案，但这种集成在保护敏感数据（如零件设计和材料成分）方面带来了关键挑战。差分隐私通过引入数学可控的噪声，在数据效用与隐私之间提供了平衡。然而，黑盒人工智能模型往往掩盖了这种噪声如何影响模型精度，使得隐私-精度权衡的优化变得复杂。本研究提出了差分隐私-超维计算框架，这是一种结合可解释人工智能与向量符号范式的新方法，通过信噪比指标量化和预测噪声对精度的影响。DP-HD框架能够精确调整差分隐私噪声水平，确保隐私与性能之间的最佳平衡。该框架已使用真实增材制造数据进行了验证，证明了其在工业环境中的适用性。实验结果表明，即使在显著噪声条件下，DP-HD框架在增材制造异常检测中能够以强大的隐私保护实现最先进的精度。除了增材制造领域，DP-HD框架在医疗健康、金融服务和政府数据管理等隐私敏感领域具有广阔的应用前景，这些领域在保持高性能的同时保护敏感数据至关重要。