Quantum Scheme Achieves Secure Data Analysis with Revocation and Keyword Search in Mobile Cloud Computing

The increasing demand for large-scale data processing on resource-limited mobile devices drives the development of Mobile Cloud Computing, but also introduces significant challenges to data privacy and security. Yue Han, Jinguang Han, and Jianying Zhou, from Southeast University and the Singapore University of Technology and Design, address these concerns with a new scheme for secure and efficient data analysis. Their research presents a method that allows mobile devices to perform keyword searches and complex computations on encrypted data stored in the cloud, without compromising privacy or revealing sensitive information. Crucially, the team’s approach incorporates robust revocation mechanisms to invalidate compromised access and minimises the computational burden on mobile devices by outsourcing most processing to the cloud, while also enabling temporary delegation of rights, representing a significant step towards practical and secure mobile cloud computing applications. Privacy Preserving EHR Computing with Access Control This research introduces a novel scheme for privacy-preserving data processing in cloud-fog based Electronic Health Record (EHR) systems.

The team addresses the challenges of secure and efficient data handling in distributed environments, with a strong focus on protecting sensitive patient information. The scheme incorporates inner product functional encryption, which allows computations on encrypted data without revealing the underlying values, alongside a fine-grained revocation mechanism for efficient access control. This bilateral access control allows both data owners and users to manage data access effectively, leveraging the scalability and efficiency of cloud-fog computing. The system also provides traceability, enabling monitoring of data access and modifications.

The team engineered a new scheme based on inner product functional encryption, specifically designed for secure EHR processing in cloud-fog environments. This approach enhances security against various attacks and improves computational efficiency. The scheme’s flexible design accommodates changing requirements and growing data volumes. The research relies heavily on lattice-based cryptography, a promising approach to securing data against future quantum computing threats, and builds upon existing research in searchable encryption, public-key encryption, attribute-based encryption, functional encryption, and cloud and fog computing, offering a significant advancement in EHR security and privacy.

Encrypted Keyword Search with Revocable Access Scientists have developed a new data analysis scheme, EQDDA-RKS, designed for secure computation in mobile cloud computing environments. This work prioritises data privacy, efficient resource use, and resilience against emerging quantum computing threats. The scheme enables mobile devices to perform keyword searches and compute inner product values over encrypted data without revealing sensitive information. To minimise the computational burden on mobile devices, the team outsources most processing tasks to a cloud server, requiring mobile devices to interact with a central authority only once for initial authorisation. A key innovation lies in the scheme’s ability to support fine-grained revocation, allowing specific function computation rights to be revoked without affecting all access. This is achieved through the use of short-term transformation keys, applied to ciphertexts before decryption by authorised users.

The team also addressed the issue of mobile device unavailability by developing a temporary delegation mechanism, allowing devices to transfer keyword search and function computation rights to another device when offline or depleted of power. This delegation relies on embedding timestamps into keyword trapdoors, function keys, and ciphertexts, ensuring computations are valid only when timestamps align. The scheme is built upon lattice-based cryptography, providing robust protection against quantum attacks, chosen plaintext attacks, chosen keyword attacks, and offline keyword guessing attacks. Comprehensive theoretical analysis and experiments demonstrate the efficiency of the proposed scheme, establishing its suitability for resource-constrained mobile cloud computing scenarios. Fine-Grained Revocation for Mobile Cloud Security Scientists have developed an efficient and quantum-resistant data analysis scheme, EQDDA-RKS, designed for mobile cloud computing environments. This work addresses critical challenges in maintaining data privacy and security while enabling powerful data processing on resource-constrained mobile devices. The scheme allows mobile users to perform keyword searches and compute inner product values over encrypted data without revealing the underlying sensitive information. Experiments demonstrate that the proposed system successfully balances data confidentiality with usability, protecting personal data like photos, behavioural data, and health records. A key achievement of this research is the implementation of fine-grained revocation, allowing specific function computation rights to be revoked without affecting all data access. This targeted approach enhances security and control over sensitive information. To minimise the burden on mobile devices, the majority of computational workload is outsourced to a cloud server, requiring users to interact with a central authority only once. Measurements confirm that this design significantly reduces the communication overhead for mobile users. Furthermore, the team incorporated a delegation mechanism, enabling temporary transfer of keyword search and function computation rights to another device when a user’s device is unavailable due to power depletion or being offline. This ensures continuous data access and processing even in challenging mobile environments. The scheme is built upon lattice-based cryptography, providing robust protection against quantum attacks, a growing threat to data security.

This research represents a significant advancement in secure data handling for mobile cloud computing, offering a practical solution for preserving privacy and enabling efficient data analysis.

Secure Mobile Cloud Data Analysis Demonstrated This research presents an efficient scheme for secure data analysis in mobile cloud computing, addressing challenges related to data privacy, access control, and computational limitations of mobile devices.

The team developed a system where mobile users can perform keyword searches and inner product computations on encrypted data without revealing sensitive information, even if a user’s access key is compromised. A key achievement is the ability to temporarily delegate data access rights, ensuring continued operation even with device limitations like power depletion. The scheme’s security was formally proven against several potential attacks, and analysis demonstrates a significant reduction in communication overhead between mobile devices and central authorities, making it well-suited for resource-constrained mobile environments. Unlike some existing approaches, the number of interactions does not increase with the number of functions performed. The authors acknowledge that their current implementation relies on specific cryptographic assumptions, and future work will focus on enhancing efficiency by exploring alternative assumptions such as the Ring Learning with Errors problem.

This research, supported by several national and provincial science foundations, contributes a practical and secure solution for data processing in increasingly mobile and connected environments. 👉 More information 🗞 Efficient Quantum-resistant Delegable Data Analysis Scheme with Revocation and Keyword Search in Mobile Cloud Computing 🧠 ArXiv: https://arxiv.org/abs/2512.12917 Tags: