The vast amount of sensor data provided by Industrial Internet of Things (IIoT) systems needs to be processed in real time process. Besides the technical challenge, there are also compliance issues that need to be taken into account. Recently, a large number of regulations have been introduced in order to protect the privacy of citizens, e.g. GDPR and IEC 62443. Existing works on anomaly detection in the IIoT environment have to decrypt the sensor data in order to apply the anomaly detection algorithm, exposing sensitive operational information. In this paper, the proposed framework is based on the CKKS homomorphic encryption scheme, is proposed. The framework enables the application of an anomaly detection algorithm directly in the encrypted data. With SHADE, the multivariate Mahalanobis distance metric can be calculated in the ciphertext domain, and the encrypted comparison result can be evaluated using a Chebyshev polynomial approximation for the threshold computation. The performance of SHADE is evaluated using synthetic IIoT datasets. The experimental results show that the proposed SHADE framework is able to achieve an Area under Curve (AUC) of 0.99 or even higher than its plaintext counterparts with only less than (ΔAUC < 0.0001) loss in AUC, while maintaining a very low false positive rate and enabling real-time processing.