In real-world applications, anomaly detection tasks are critically important. For example, fraud detection for the financial domains and the diagnosis of diseases for the medical domains require highly accurate predictions, as errors can lead to severe consequences. These tasks often rely on sensitive personal data, making it necessary to apply privacy-preserving techniques. However, applying privacy-preserving techniques directly degrades performance. To mitigate this issue, the minority class in an imbalanced dataset can be upsampled to improve balance. In this paper, we propose a differentially private upsampling method using a kernel-based support function for imbalanced datasets. The proposed method employs kernel support vector domain description to estimate the distribution of minority class data under differential privacy constraints, generating synthetic instances based on gradient methods. Additionally, we propose a filtering process that leverages the support function of the majority class data to refine the generated samples without additional privacy loss. Experimental results on real-world datasets demonstrate that the proposed method maintains robust privacy guarantees and achieves superior performance in minority class metrics, comparable to non-private methods.Co-authors: Yujin Choi, Jinseong Park, Youngjoo Park, Jaewook Lee

We propose a new privacy-preserving mean-variance optimization model, merging Multi-Party Computation (MPC) with Homomorphic Encryption (HE) through an innovative method. Empirical tests show our model outperforms existing ones in privacy optimization, overcoming limitations regarding complex constraints. We highlight three findings: our model (i) outperforms others in privacy-preserving utility maximization with no-short-selling constraint; (ii) remains effective under complex box constraints, whereas the existing model entirely collapses; and (iii) achieves close alignment with the optimal portfolio from an economic perspective, providing high computational efficiency. It proves to be an effective solution for privacy optimization, a key aspect in mitigating ESG risks.Co-authors: Hyungjin Ko, Jaewook Lee

In IoT-enabled industrial environments, ensuring the privacy and security of operational data is paramount for fault diagnosis systems. This study presents a novel framework that seamlessly integrates homomorphic encryption (HE) with deep learning to achieve secure and efficient fault diagnosis for industrial bearings. By performing computations directly on encrypted sensor data, the framework guarantees full data confidentiality throughout the diagnostic process without requiring decryption. Key technical contributions of this work include the development of a minimax polynomial approximation for ReLU activations, which enhances diagnostic accuracy while preserving efficiency, and the design of an efficient 1D convolution method that combines two existing HE convolution techniques for optimal performance. Additionally, the framework incorporates frequency-domain optimizations using the Discrete Fourier Transform (DFT), which significantly enhance processing efficiency. The proposed model was trained on the CWRU bearing dataset and validated on a private dataset, achieving a diagnostic accuracy of 95.92%, comparable to state-of-the-art models operating on plaintext data. Furthermore, the DFT-based optimizations reduced inference time by nearly threefold while maintaining superior accuracy, underscoring the framework’s potential to provide secure and efficient fault diagnosis for industrial applications.Co-authors: Hoki Kim, Youngdoo Son