Electronic Health Record (EHR) and its privacy have attracted widespread attention with the development of the healthcare industry in recent years. As locking medical data in a single healthcare center causes information isolation, healthcare centers are motivated to build medical data sharing systems. However, existing systems highly rely on the trusted centralized servers, which are vulnerable to distributed denial of service (DDoS) attacks and the single point of failure. Moreover, it is a non-trivial matter to authorize multiple users to search and access EHR in a privacy-preserving manner. In this paper, we propose MedShare, a decentralized framework for secure EHR sharing. Our design utilizes the smart contract technique of blockchain to establish a trusted platform for healthcare centers to share their encrypted EHR. Considering that fine-grained access control is essential in practical EHR sharing service, we devise a constant-size attribute-based encryption (ABE) scheme, where the access policy is embedded in search result on the blockchain. Besides, we propose an efficient scheme that enables authorized MedShare users to perform multi-keyword boolean search operations over encrypted EHR. We formally analyze the security strengths and implement the system prototype on Ethereum. Evaluation results demonstrate that MedShare is efficient for EHR sharing.

Machine unlearning as a fundamental requirement in Machine-Learning-as-a-Service (MLaaS) has been extensively studied with increasing concerns about data privacy. It requires MLaaS providers should delete training data upon user requests. Unfortunately, none of the existing studies can efficiently achieve machine unlearning validation while preserving the retraining efficiency and the service quality after data deletion. Besides, how to craft the validation scheme to prevent providers from spoofing validation by forging proofs remains under-explored. In this paper, we introduce a backdoor-assisted validation scheme for machine unlearning. The proposed design is built from the ingenious combination of backdoor triggers and incremental learning to assist users in verifying proofs of machine unlearning without compromising performance and service quality. We propose to embed invisible markers based on backdoor triggers into privacy-sensitive data to prevent MLaaS providers from distinguishing poisoned data for validation spoofing. Users can use prediction results to determine whether providers comply with data deletion requests. Besides, we incorporate our validation scheme into an efficient incremental learning approach via our index structure to further facilitate the performance of retraining after data deletion. Evaluation results on real-world datasets confirm the efficiency and effectiveness of our proposed verifiable machine unlearning scheme.

The increasing popularity of Online Ride-hailing (ORH) services has greatly facilitated our daily travel. It enables a rider to easily request the nearest driver through mobile devices in a short time. However, existing ORH systems require the collection of users' location information and thus raise critical privacy concerns. While several privacy-preserving solutions for ORH service have been proposed, most of existing schemes rely on an additional trusted party to compute the distance between a rider and a driver. Such a security assumption cannot fully address the privacy concerns for practical deployment. In this paper, we present a new ride-matching scheme for ORH systems, which allows privacy-preserving and effective distance calculation without involving a third-party server. Our proposed scheme enables ORH systems to securely compute the user distance while protecting the location privacy of both riders and drivers. Specifically, we resort to state-of-the-art distance calculation techniques based on Road Network Embedding (RNE), and show how to uniquely bridge cryptographic primitives like Property-preserving Hash (PPH) with RNE in depth to support privacy-preserving ride-matching services. Moreover, we also propose an optimized design to improve the matching efficiency. We formally analyze the security strengths and implement the system prototype. Evaluation results demonstrate that our design is secure and efficient for ORH systems.

With the rapid development of cloud computing, it is popular for data owners to outsource massive data to the cloud server for data sharing. To protect the privacy of sensitive data, many searchable encryption schemes are proposed. However, most of the existing studies focus on the single-owner model. In practice, users need to query data from distributed owners one by one, which inevitably brings great communication and computation overheads. Moreover, it lacks a secure scheme that realizes the access control requirements of individual owners. In this article, we propose AESM $^{2}$ , a new attribute-based encrypted search with ownership enhancement scheme for multi-owner and multi-user distributed systems. Our design enables users to search data from authorized owners with only one trapdoor. Owners can enforce owner level permission on users and encrypt their data individually with fine-grained attribute level permission. For practical consideration, we further devise an efficient revocation method of the owner level permission for users, where ciphertexts do not need to be updated. We formally define and prove the security of our design. Moreover, we implement a system prototype and analyze the performance from theoretical and experimental aspects. The evaluation results demonstrate that our scheme is effective and efficient.

Internet of Medical Things (IoMT) has gained significant research focus in both academic and medical institutions. Nevertheless, the sensitive data involved in IoMT raises concerns regarding user validation and data privacy. To address these concerns, certificateless signcryption (CLSC) has emerged as a promising solution, offering authenticity, confidentiality, and unforgeability. Unfortunately, most existing CLSC schemes are impractical for IoMT due to their heavy computational and storage requirements. Additionally, these schemes are vulnerable to quantum computing attacks. Therefore, research focusing on designing an efficient post-quantum CLSC scheme is still farreaching. In this work, we propose PQ-CLSCL, a novel post-quantum CLSC scheme with linkability for IoMT. Our proposed design facilitates secure transmission of medical data between physicians and patients, effectively validating user legitimacy and minimizing the risk of private information leakage. To achieve this, we leverage lattice sampling algorithms and hash functions to generate the partial secret key, and then employ the sign-then-encrypt method as well as design link label. We also formalize and prove the security of our design, including indistinguishability against chosen-ciphertext attacks (IND-CCA2), existential unforgeability against chosen-message attacks (EU-CMA), and linkability. Finally, through comprehensive performance evaluation, our computation overhead is just 5% of some other existing schemes. The evaluation results demonstrate that our solution is practical and efficient.