Anomaly detection plays a crucial role in ensuring the health and well-being of mothers by monitoring various health parameters in real-time. Unlike static health data, maternal health monitoring data are dynamic, possessing properties such as infiniteness, correlations, and changes in data distribution. These properties present challenges for current anomaly detection approaches. Firstly, saving the entire dataset is impractical due to its infiniteness. Secondly, existing methods often fail to consider correlations between different health metrics. Thirdly, changes in data distribution are not adequately addressed because of the absence of model update and change detection strategies. To tackle these issues, we propose Maternal health Anomaly Detection (MAD), a groundbreaking method combines sliding window mechanisms, model adjustments, and change detection strategies within Locality-Sensitive Hashing isolation Forest (LSHiForest) to enhance anomaly detection accuracy and efficiency while ensuring better scalability. This method, termed MAD, includes several key features: (1) The sliding window approach effectively manages the endless nature of data streams; (2) Incorporating Singular Value Decomposition (SVD) takes into account the interrelationships among different health metrics; (3) The change detection process, along with model updates, swiftly recognizes shifts in data distribution and retrains models as needed. Comprehensive experiments on relevant maternal health datasets validate the performance of MAD. Results indicate that MAD outperforms existing methods in terms of accuracy, efficiency, and scalability, making it a promising solution for maternal health monitoring.

As the main parent and guardian, mothers are often concerned with the study performance of their children. More specifically, most mothers are eager to know the concrete examination scores of their children. However, with the continuous progress of modern education systems, most schools or teachers have now been forbidden to release sensitive student examination scores to the public due to privacy concerns, which has made it infeasible for mothers to know the real study level or examination performance of their children. Therefore, a conflict has come to exist between teachers and mothers, which harms the general growing up of students in their study. In view of this challenge, we propose a Privacy-aware Examination Results Ranking (PERR) method to attempt at balancing teachers’ privacy disclosure concerns and the mothers’ concerns over their children’s examination performance. By drawing on a relevant case study, we prove the effectiveness of the proposed PERR method in evaluating and ranking students according to their examination scores while at the same time securing sensitive student information.