Yunfei Yao

The idea of multi-keyword ranked search over encrypted cloud data has attracted considerable attention in recent studies, as it allows users to securely and efficiently retrieve highly relevant results. Traditional methods improve search efficiency by incorporating the K-means clustering algorithm. However, when applied to large-scale datasets, K-means can become computationally expensive. This paper introduces a multi-keyword ranked search method, SAO-KRS, which leverages the snow ablation optimizer (SAO) to enhance clustering performance. The approach begins with principal component analysis (PCA) to reduce the dimensionality of high-dimensional data, followed by clustering the reduced data using SAO, which reduces clustering overhead massively. By incorporating a heuristic best-first search algorithm over index trees, the scheme achieves reduced computational cost with high retrieval accuracy. In the best-case scenario, the proposed method achieves up to 21 times faster clustering and 2.7 times faster searching compared to the traditional K-means approach. Extensive experimental results verify that this method significantly improves clustering efficiency while ensuring both search speed and accuracy.

The widespread adoption of consumer electronics has enabled patients to easily schedule appointments with doctors through online platforms, advancing the development of telemedicine. Utilizing AI technology, these platforms assist physicians in symptom analysis and providing precise recommendations, overcoming geographical barriers and achieving a balanced utilization of medical resources. Moreover, the advancement of 5G and the anticipated 6G networks has provided the capability for swift and accurate execution of cloud-based AI applications. However, online medical image consultations based on plaintext carry the risk of significant privacy breaches. By leveraging secret sharing technology, privacy leakage issues can be effectively addressed. However, the application of this technology alters the visual representation of image data. In multi-party scenarios, each party possesses only one secret share, which makes verifying the authenticity of the image challenging. To address this issue, we propose the Secure Federated Image Authentication system, which enables the authentication of encrypted images provided by consultants before the provision of services. This system employs technologies such as random feature signatures, secret sharing, and distributed reversible data hiding to achieve federated authentication of images. Through security analysis and performance evaluation, our method has demonstrated satisfactory results in terms of security and availability.