Multi-party Secure Comparison of Strings Based on Outsourced Computation

Data is an important production factor in the era of digital economy. Privacy computing can ensure that data providers do not disclose sensitive data, carry out multi-party joint analysis and computation, securely and privately complete the full excavation of data value in the process of circulation, sharing, fusion, and calculation, which has become a popular research topic. String comparison is one of the common operations in data processing. To address the string comparison problem in multi-party scenarios, we propose an algorithm for secure string comparison based on outsourced computation. The algorithm encodes the strings with one hot encoding scheme and encrypts the encoded strings using an XOR homomorphic encryption scheme. The proposed algorithm achieves efficient and secure string comparison and counts the number of different characters with the help of a cloud-assisted server. The proposed scheme is implemented and verified using the new coronavirus gene sequence as the comparison string, and the performance is compared with that of a state-of-the-art security framework. Experiments show that the proposed algorithm can effectively improve the string comparison speed and obtain correct comparison results without compromising data privacy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Dual Arrhythmia Classification Algorithm Based on Deep Learning and Attention Mechanism Incorporating Morphological-temporal Information

In the context of increasing medical resource constraints and the global pandemic of COVID-19, the acquisition and automatic diagnosis of electrocardiogram (ECG) signal at home is becoming more and more important. In this paper, we propose a dual arrhythmia classification algorithm for edge-cloud collaboration. We first design a lightweight single-lead ECG signal binary classification model incorporating RR intervals that can be deployed at the edge, which achieves lightweight ECG feature extraction by using depthwise separable convolution and positional attention, and fuses RR interval features to the fully connected layer to achieve normal or abnormal classification of ECG heartbeats. For heartbeats classified as abnormal using the above model, we design a dual-branch arrhythmia multi-classification model with channel and spatial dual attention that integrates simple convolutional neural network (CNN) modules that can be deployed in a cloud artificial intelligence (AI) server to perform accurate classification of abnormal ECG heartbeats, where the input of one branch is a heartbeat signal and the input of the other branch is an ECG segment containing adjacent R-peaks. The experimental results based on the MIT-BIH arrhythmia database demonstrate that our binary classification model achieves an average accuracy of 99.80% and the multi-classification model achieves an average accuracy of 99.71%, and our method ensures a high enough accuracy while performing dual analysis to make the analysis results more reliable. © 2022 IEEE.