Currently, blockchain technology, which is decentralized and may provide tamper-resistance to recorded data, is experiencing exponential growth in industry and research. In this paper, we propose the MIStore, a blockchain-based medical insurance storage system. Due to blockchain’s the property of tamper-resistance, MIStore may provide a high-credibility to users. In a basic instance of the system, there are a hospital, patient, insurance company and n servers. Specifically, the hospital performs a (t, n)-threshold MIStore protocol among the n servers. For the protocol, any node of the blockchain may join the protocol to be a server if the node and the hospital wish. Patient’s spending data is stored by the hospital in the blockchain and is protected by the n servers. Any t servers may help the insurance company to obtain a sum of a part of the patient’s spending data, which servers can perform homomorphic computations on. However, the n servers cannot learn anything from the patient’s spending data, which recorded in the blockchain, forever as long as more than n − t servers are honest. Besides, because most of verifications are performed by record-nodes and all related data is stored at the blockchain, thus the insurance company, servers and the hospital only need small memory and CPU. Finally, we deploy the MIStore on the Ethererum blockchain and give the corresponding performance evaluation.

In the face of massive data on the Internet, users often “lost themselves”. Personalized recommendation technology has made breakthroughs in areas such as e-commerce, advertising, audio and video recommendation in recent years. Due to the inherent characteristics of network news, such as the massive data, heterogeneity, update and change fast, timeliness and strong geographical awareness and so on, the progress of personalized recommendation technology in the field of news lags behind the above areas. And it cannot meet the requirements in news field entirely. Therefore, it is the main task of the current news recommendation system to integrate the existing personalized recommendation technologies into the news recommendation field, to study how to handle massive heterogeneous news data, to construct an optimal user preference model, and to improve the overall performance of the personalized news recommendation. This paper presents the state-of-the-art personalized news recommendation technologies in recent years, and analyzes the advantages and disadvantages of the mainstream technology based on seven main directions. Finally, the open issues in the development of personalized news recommendation technology are analyzed and concluded, hoping to guide the related work in the future.

As the functions of connected vehicles become more complicated, the amount and type of data they generate during driving are increasing. Sharing the data among connected vehicles can improve users' driving experience and reduce traffic pressure. However, the leakage of users' private information caused by data sharing may harm the interest of vehicle users and even endanger their lives. Meanwhile, since the quality of data collected by vehicles varies, high-quality data sharing means users get more reliable services, while low-quality data can reduce service reliability, drive experience, or cause traffic accidents. Therefore, developing an effective and highly reliable privacy protection scheme for shared data becomes a pressing problem to be solved. At the same time, since data sharing requires low latency, it is also a challenge to introduce a privacy protection mechanism without significant delay. In this paper, we propose a decentralized federated learning-based data sharing scheme that provides strong privacy protection for data and improves the system's robustness. In particular, we decouple the data request process from the data sharing process to improve the sharing efficiency. Then we propose a TOP-K-based nodes selection scheme to improve the accuracy of the trained models and ensure data reliability. The security analysis shows that the scheme can resist attacks and achieve secure data sharing. Finally, the scheme's effectiveness and the model prediction's efficiency are verified through experiments. The results show that the scheme has high accuracy, efficiency, and security.

Efficient medical image sampling and transferring becomes one of key research areas in computer science and healthcare application industries. In particular, the technique of body-area networking (BAN) and personal-area networking (PAN) are very useful in various image-based medical monitoring systems that cover a wide range of healthcare services, such as early detecting of emergency conditions and remote online instructing of surgeries. However, medical images are highly privacy sensitive and redundant. Thus proper protection on privacy and secure data aggregation/compression are also highly expected in medical image processing. Based on compressive sensing (CS) theory, we conceive a so-called “one-stone-three-bird" solution for medical image acquisition and transmission in this paper: The size of the original medical images can be reduced to 20%, the resulted images have very well confidentiality and supporting additively homomorphic aggregation.

In the Internet of Things (IoT), cyber physical system (CPS) has achieved a sgreat success in a wide range of distributed integration environments. In the cyber physical system (CPS), interconnected sensing devices collect data in the surrounding environment and send data to all interested nodes through the network, thereby sharing all node data. This process can be implemented by using a publish/subscribe (pub/sub for short) system. Providing the basic security mechanisms such as authorization and confidentiality is a challenge due to the loose coupling of subscribers and publishers in such pub/sub system. At the meanwhile, the existing IoT ecosystem mostly relies on a centralized server and thus faces the problem of single point failure. Thus it is interesting to realize a brokerless or decentralized pub/sub model. Inspired by this motivation, this paper mainly proposes a privacy-preserving publish/subscribe model by using the blockchain technique, which evades the centralized trustroot setting and the problem of single point failure. Another key point of our proposal is that the primitive of public key encryption with equality test (PKEwET) is employed to enable all the required authorization, communication and topics matching can be finished in a confidential manner. Finally, a lightweight prototype of our proposal is implemented by using web3j, and the security and efficiency analysis are also presented.

Mobile healthcare social networks (MHSN) integrated with connected medical sensors and cloud-based health data storage provide preventive and curative health services in smart cities. The fusion of social data together with real-time health data facilitates a novel paradigm of healthcare big data analysis. However, the collaboration of healthcare and social network service providers may pose a series of security and privacy issues. In this paper, we propose a secure health and social data sharing and collaboration scheme in MHSN. To preserve the data privacy, we realize secure and fine-grained health data and social data sharing with attribute-based encryption and identity-based broadcast encryption techniques, respectively, which allows patients to share their private personal data securely. In order to achieve enhanced data collaboration, we allow the healthcare analyzers to access both the reencrypted health data and the social data with authorization from the data owner based on proxy reencryption. Specifically, most of the health data encryption and decryption computations are outsourced from resource-constrained mobile devices to a health cloud, and the decryption of the healthcare analyzer incurs a low cost. The security and performance analysis results show the security and efficiency of our scheme.