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Abstract Machine learning (ML) models are used for in-situ monitoring in additive manufacturing (AM) for defect detection. However, sensitive information stored in ML models, such as part designs, is at risk of data leakage due to unauthorized access. To address this, differential privacy (DP) introduces noise into ML, outperforming cryptography, which is slow, and data anonymization, which does not guarantee privacy. While DP enhances privacy, it reduces the precision of defect detection. This paper proposes combining DP with Hyperdimensional Computing (HDC), a brain-inspired model that memorizes training sample information in a large hyperspace, to optimize real-time monitoring in AM while protecting privacy. Adding DP noise to the HDC model protects sensitive information without compromising defect detection accuracy. Our studies demonstrate the effectiveness of this approach in monitoring anomalies, such as overhangs, using high-speed melt pool data analysis. With a privacy budget set at 1, our model achieved an F-score of 94.30%, surpassing traditional models like ResNet50, DenseNet201, EfficientNet B2, and AlexNet, which have performance up to 66%. Thus, the intersection of DP and HDC promises accurate defect detection and protection of sensitive information in AM. The proposed method can also be extended to other AM processes, such as fused filament fabrication.