Decision tree classification has become a prevailing technique for online diagnosis services. By outsourcing computation intensive tasks to a cloud server, cloud-assisted online diagnosis services are better ways for cases that the storage and computation requirements exceed the capability of medical institutions. With privacy concerns as well as intellectual property protection issues, the valuable diagnosis classifier and the sensitive user data should be protected against the cloud server. In this paper, we identify a work-flow for cloud-assisted online diagnosis services. We propose an efficient and secure decision tree classification scheme in the proposed work-flow. Specifically, the medical institution transforms a locally pre-trained decision tree classifier to a decision table, and later uses searchable symmetric encryption to encrypt the decision table. Then, the encrypted table is outsourced to the cloud server, and a user can submit encrypted physiological features to the cloud server and obtain an encrypted diagnosis prediction back. We provide formal security proofs to demonstrate that our scheme protects the confidentiality of the decision tree classifier and the user's data. The performance analysis shows that our scheme achieves faster-than-linear classification speed. Experimental evaluations show that our scheme requires several micro-seconds to process a diagnosis request in the tested datasets.

In connected autonomous vehicles (CAVs), the driving safety can be greatly deteriorated, in the presence of occlusions which are adverse to CAVs’ perception of region-of-interest (RoI). Collaborative perception on the basis the information sharing of occlusions among CAVs, in a real-time and accurate manner, provides a means of the occlusion-free RoI perception for safe driving. In this paper, we propose a novel framework of C ollaborative O cclusion- f ree P erception ( COFP ) in CAVs, to regain the real-time and accurate occlusion awareness. The innovative COFP targets two goals: well-balanced computation resource allocation, as well as fast and high-quality RoI information fusion. Specifically, the resource allocation problem, with the objective of minimizing CAVs’ completion delay, is formulated as a multi-player continuous potential game and solved by a better response dynamics (BRD) algorithm. The RoI information fusion, with the objective of maximizing the overall object depiction quality, is formulated as a combinatorial optimization problem, and solved by a modified discrete salp swarm (MDSSA) algorithm. Experimental results show that the proposed COFP with 5 GHz computing power can achieve full occlusion awareness for CAVs with 69.61% completion time reduction and 19.03% fusion quality improvement, compared to the existing methods.

Privacy-preserving online multi-task assignment is a crucial aspect of spatial crowdsensing on untrusted platforms, where multiple real-time tasks are allocated to appropriate workers in a privacy-preserving manner. While existing schemes ensure the privacy of tasks and users, they seldom focus on minimizing the total moving distances for crowdsensing workers when assigning multiple tasks in real time, which adversely impacts the efficiency of online multi-task assignments. To address this issue, we propose POTA, the first privacy-preserving online multi-task assignment scheme with path planning that minimizes the total moving distances for crowdsensing workers without additional noise. POTA cryptographically implements the extended minimum-cost flow model, which models the encrypted data of workers and tasks in a graph and later produces optimized routing. With such a secure path-planning component, POTA reduces the total moving distances by $25.19\%-52.78\%$ in the tested dataset compared with the state-of-the-art schemes with obfuscated path planning. Security analysis proves that POTA guarantees the confidentiality of sensitive data, a stronger security property than introducing obfuscation to sensitive data. Experimental evaluations on real-world datasets demonstrate the feasibility of POTA in terms of running time and its ability to achieve minimized total moving distances.

Recently, increasing personal data has been stored in blockchain databases, ensuring data integrity by consensus. Although transparent and immutable blockchains are mainly adopted, the need to deploy preferences on which users can read and edit the data is growing in importance. Based on chameleon hashes, recent blockchains support editability governance but can hardly prevent data breaches because the data is readable to all participants in plaintexts. This motivates us to propose NANO, the first permissioned blockchain database that provides downward compatible readability and editability governance (i.e., users who can edit the data can also read the data). Two challenges are protecting policy privacy and efficiently revoking malicious users (e.g., users who abuse their editability privileges). The punchline is leveraging Newton's interpolation formula-based secret sharing to hide policies into polynomial parameters and govern the distribution of data decryption keys and chameleon hash trapdoors. Inspired by proxy re-encryption, NANO integrates unique user symbols into user keys, achieving linear user revocation overhead. Security analysis proves that NANO provides comprehensive privacy preservation under the chosen-ciphertext attack. Experiments on the FISCO blockchain platform demonstrate that compared with state-of-the-art related solutions, NANO achieves a 7× improvement on average regarding computational costs, gas consumption, and communication overhead.

Support vector machine (SVM) classification techniques have been widely adopted for building clinical decision models. In cloud-based remote clinical decision services, a healthcare center outsources the clinical decision model to a cloud server, which then provides remote clinical decision services to end users. In this article, we propose a practical and secure SVM classification scheme ( ${\sf SSVMC}$ ) for cloud-based remote clinical decision services. Specifically, we first extract SVM decision rules from an SVM classifier. Then, we leverage symmetric key encryption to protect the confidentiality of medical data and prevent the cloud service provider from misusing intellectual property of the outsourced clinical model. Finally, we build encrypted indexes to achieve efficient SVM classification. We define a leakage function, formulate a security definition, and provide a simulation-based security proof for ${\sf SSVMC}$ . The performance analysis demonstrates that ${\sf SSVMC}$ achieves linear computational complexity when an SVM classifier (a.k.a., the clinical decision model) is pre-trained. The simulations evaluate the impact of several parameters on time costs. The experimental evaluations show the performance differences between ${\sf SSVMC}$ and several existing schemes in terms of time costs, storage costs, communication costs, and precisions in a real-world clinical dataset, which demonstrate that ${\sf SSVMC}$ is computationally efficient with high decision accuracy.