Blockchain-enabled Internet of Things (IoT) provides a secure sharing of data and resources to the various miners of the IoT network, removes centralized control, and can overcome part of the existing challenges in traditional IoT. However, the IoT ecosystem faces some great challenges in terms of security, such as privacy leaking, eavesdropping, and so on, which seriously impede the deployment of the IoT ecosystem. Verifiable searchable encryption (SE) can make data owners (DOs) in the IoT dispel concerns about privacy and make data users (DUs) believe they get correct search results. For the blockchain-enabled IoT, this article proposes an efficient verification SE scheme with aggregation authorization and trusted revocation. With this scheme, DOs are willing to share their data to DUs for reward in a secure, efficient, and trusted way. For the DU, the DO can generate an aggregating key of a subset of encrypted documents, and then the DU has the privilege to search these documents and to verify the search results. By utilizing the trusted execution environment, the DO can revoke the search privilege of the DU. We present the analysis to show our proposed scheme achieves confidentiality, soundness, and fair payment at the same time. With the performance evaluations, we prove our proposal practical for blockchain-enabled IoT in terms of computational and smart contracts overhead.

With the rapid development of smart cities, vehicles equipped with various sensors can effectively sense traffic, thus forming a crowdsensing paradigm for the Intelligent Transportation System (ITS). Although mobile crowdsensing in ITS has broad application advantages, it still faces many challenges, such as single point of failure, inefficient independent task allocation, and the inability to deal with safety emergency tasks in time. To handle the above issues, we establish a decentralized ITS architecture based on blockchain and propose the concurrent tasks assignment problem proved to be NP-hard and safety emergency tasks assignment problem. Then we propose Reinforcement Learning (RL) based concurrent tasks and the safety emergency tasks assignment method, which can maximize the utility of concurrent tasks based on satisfying the requirements of safety emergency tasks. Simulation results demonstrate the effectiveness of the proposed methods.

While the Internet of things brings convenience to people's lives, it will also bring people hidden worries about data security. As an important barrier to protect data security, identity authentication is widely used in the Internet of things. However, it is necessary to protect users' identity privacy while authenticating their identity. Anonymous authentication technology is often used to solve the contradiction between legitimacy and privacy in the authentication process. The existing anonymous authentication scheme has many problems in practical application such as the inability to achieve complete anonymity, the high computational complexity of the algorithm, and the corruption of the central authority. Aiming at the privacy of authentication, we propose Zero-Cerd, a self-blindable anonymous authentication system based on blockchain and dynamic accumulator. The self-blinding properties of the credential enable the users themselves to generate a new validly pseudonymous credential. With the help of zero-knowledge proof technology, users can prove the validity of their credentials without disclosing any information. Security analysis shows that our scheme has achieved the expected security objectives. Compared with the existing schemes, our scheme has the advantages of complete anonymity and high efficiency, and is more suitable for IoT applications with privacy protection requirements.

Crowdsourcing for healthcare, which is an application of crowd intelligence, has become a novel and important auxiliary way for traditional healthcare, showing a huge application perspective. In a crowdsourcing platform for healthcare, patients can act as requesters who recruit workers, such as doctors, to provide professional advice by posting a task. However, privacy concerns pose a significant obstacle for patients willing to participate in crowdsourcing, as task data often contain sensitive personal information. To address this issue, we propose a novel attribute-based, lightweight, and dynamic fine-grained worker selection scheme, called ABCrowdMed, with privacy-preserving features. With this scheme, requesters can select workers in a non-interactive way by using a novel CP-ABE scheme that incorporates online/offline encryption, verifiable outsourcing decryption, revocation, and hidden policy properties. Additionally, requesters can revoke and update their tasks by withdrawing some workers' decryption privileges. Participants can also release the computation burden with the aid of a third-party server. The proposed scheme's security has been proven to be selectively secure under the decisional $ (q-1)$ assumption and satisfies forward/backward security. The performance of ABCrowdMed has been evaluated and compared with state-of-art schemes, with the results demonstrating that our scheme achieves the lowest computation and is suitable for resource-constrained settings.

Premature rupture of membranes (PROM) is a common pregnancy complication that frequently results in maternal and perinatal morbidity. The present methods for diagnosing PROM do not satisfy clinical requirements. The present study aimed to examine the proteome profile of amniotic fluid (AF) and maternal plasma, screen unique proteins in AF, and evaluate their diagnostic value for diagnosing PROM. The proteome profiles of AF and maternal plasma were examined via liquid chromatography coupled with tandem mass spectrometry‑based proteomic techniques. The protein expression levels of diagnostic candidates in AF, maternal plasma and vaginal fluid were determined by ELISA analysis and Magnetic Luminex® screening assays. The diagnostic value of potential biomarkers was evaluated using receiver operating characteristic curves. A lateral flow assay was developed based on colloidal gold immunochromatography technology. The present study identified 540 unique proteins in AF, 12 of which were chosen for further detection. The present results demonstrated that expression levels of pulmonary surfactant‑associated protein B, BPI fold‑containing family A member 1, zymogen granule protein 16 homolog B, EGF‑containing fibulin‑like extracellular matrix protein 1, keratin, type II cytoskeletal 4, keratin, type I cytoskeletal 19, placental protein 14 (PP14), insulin‑like growth factor‑binding protein 2, mesothelin and serpin family B member 3 were significantly higher in AF compared with in maternal plasma (P<0.01). Furthermore, PP14 was observed to have excellent diagnostic accuracy for preterm PROM (PPROM), with a respective sensitivity and specificity of 100 and 87.5% when the cutoff value was 0.008 µg/ml. The PP14‑based lateral flow assay demonstrated a visual detection threshold of 0.008 µg/ml. The results from the present study suggested that PP14 may be a novel potential biomarker for PPROM, and may be developed into a lateral flow assay for bedside application to rapidly diagnose PPROM.