Aging in Place (AiP) programs enable elderly individuals to live independently and comfortably within their homes and communities by utilizing technological innovations such as smart homes and remote healthcare monitoring. In practical AiP scenarios, data necessary for accurate health predictions are typically vertically partitioned across multiple medical institutions, raising significant privacy concerns during data integration and analysis. To address this challenge, we propose an efficient and privacy-preserving logistic regression (LR) prediction scheme tailored explicitly for vertically partitioned AiP data. Our scheme effectively combines the computational efficiency of Trusted Execution Environments (TEE) under the honest-but-curious model with cryptographic security based on the Matrix Diffie-Hellman (MDDH) assumption. Security analysis confirms that our approach provides privacy protections against honest-but-curious adversaries. Extensive experimental evaluations demonstrate that our proposed scheme achieves computational efficiency, privacy protection, and practical scalability for real-world AiP implementations.

With the global aging population rising rapidly, the Aging in Place (AiP) system has gained significant attention and is seen as a vital strategy for meeting the diverse needs of older adults. In the AiP system, Electronic Health Records (EHRs) play a critical role in managing the healthcare records of older adults. Compared to traditional medical records, EHRs in AiP include not only clinical data but also data from Internet of Things (IoT) devices, social determinants of health, and other sources, resulting in a significant increase in the volume of EHRs. Consequently, preserving the privacy of these EMRs while sharing with access control in AiP scenarios becomes a significant challenge. Many data sharing schemes based on Attribute-Based Encryption (ABE) suffer from limited computational efficiency and inadequate privacy protections. To address these issues, we propose an efficient and privacy-preserving data sharing scheme for AiP systems. Our scheme not only facilitates the sharing of large files with matched users but also conceals the policy from other users. Furthermore, our proposed scheme is secure under the semi-honest model, and experiments demonstrate its high efficiency.

As the global population continues to age rapidly, Aging in Place (AiP) solutions have become increasingly vital for enabling elderly individuals to maintain their independence and continue living comfortably in their own homes. These solutions leverage advanced technologies such as smart homes and remote health monitoring. However, in real-world AiP applications, the health data needed for accurate predictions is often spread across multiple medical institutions, which raises signficant privacy concerns when integrating and analyzing the data. To address this challenge, we propose an efficient and privacy-preserving AdaBoost learning framework for vertically partitioned AiP data by utilizing Symmetric Homomorphic Encryption (SHE) technique. To ensure compatibility with the integer-based constraints of SHE, we adopt a straightforward weight quantization strategy by representing AdaBoost sample weights as integers. This design simplifies encrypted computation and maintains the boosting mechanism’s effectiveness. Our theoretical and experimental evaluations validate both the accuracy and security of the proposed framework, highlighting its practical viability for deployment in real-world AiP systems.

Multidimensional range queries are typical database operations used to retrieve data. With the development of cloud computing, outsourcing data storage and queries to a cloud server is an attractive choice for data owners; however, this choice involves well-known privacy issues. To preserve data privacy, data should be encrypted before they are outsourced to the cloud. Therefore, exploring multidimensional range queries on encrypted data has important theoretical and practical significance. Certain privacy-preserving schemes have been proposed to support multidimensional range queries on encrypted data. However, these schemes exhibit either poor privacy performance or poor computational or communication performance. This makes such schemes impractical for resource-constrained scenarios such as Internet of Things (IoT) environments. To improve security and efficiency in making them applicable to IoT environments, we propose lightweight secure vector comparison and secure double-blind protocols as building blocks to construct an efficient scheme, named the fully privacy-preserving multidimensional range queries scheme (FPMRQ), and prove that FPMRQ can resist database reconstruction and query-recovery attacks. To improve communication efficiency, we adopt methods to pack multidimensional data into single-dimensional data and aggregate multiple data records into a single record of data. Finally, we conducted numerous experiments on real-world data sets to examine the efficiency of FPMRQ, and the experimental results show that FPMRQ significantly improves the computational efficiency (almost three orders of magnitude faster) and communication efficiency (at least$7.15\times $faster) in comparison with existing schemes with the same security level. These results demonstrate the practicality of the FPMRQ for resource-restrained environments, such as IoT.

Graphs are an essential data representation method and are widely used in many scenarios. Shortest distance query is one of the most fundamental operations on a graph and has thus attracted much research attention. While various graph encryption schemes supporting shortest distance query have been proposed, there remain considerable challenges, such as information leakage, approximate query results, and large computational errors. Furthermore, existing schemes can only address 2-hop cover shortest distance queries on encrypted graphs and do not allow shortest distance queries by others. Therefore, only the graph owner can query the shortest distance based on encrypted data. To overcome these limitations, we propose a privacy-preserving Any-Hop Cover Shortest Distance Query scheme on encrypted graphs, called AHCSDQ. Matrices are used to compute any-hop cover shortest distance (AHCSD) between any two vertices. Our scheme preprocesses graph data and store the preprocessed results in a server, and query users can retrieve the corresponding encoding value from the appropriate matrix. This scheme enable accurate AHCSD queries while protecting graph data privacy. In addition, the graph owner only needs to preprocess graph data once, and the preprocessing result can be provided to multiple users for multiple queries. Furthermore, a single shortest distance query only needs the query user one encryption operation, also significantly reduces the computational cost for query users. In this paper, we establish the security of our proposed scheme using a widely accepted simulation paradigm and present experimental results to demonstrate the high efficiency of scheme.

A constrained shortest distance (CSD) query calculates the shortest distance between two vertices of a graph and places constraints on certain factors so as not to exceed corresponding thresholds. With the development of cloud computing, outsourcing graph data storage and the computation of CSD queries to a cloud platform (CP) are an attractive choice for graph owners; however, this choice is accompanied by well-known privacy issues. Certain privacy-preserving schemes have been proposed to support CSD queries on encrypted graphs, but all such schemes consider only single-CSD queries, even though, in practical applications, users may need to set two or more constraints when executing the shortest distance query. Additionally, existing schemes disclose too much private information to the CP. To address these considerable problems, we propose a strong privacy-preserving CSD query scheme called SPCS, which realizes accurate double-CSD queries without disclosing any critical private information. We prove that SPCS does not reveal any private information to the CP except for the number of vertices and conduct numerous experiments on real-world data sets to test this scheme’s efficiency. The experimental results show that SPCS is practical, especially in its computational efficiency in the graph-encryption phase, which is higher than that of available, state-of-the-art schemes.