Traditional medical privacy data is at serious risk of disclosure, and many related cases have occurred over the years. For example, personal medical privacy data can be easily leaked to insurance companies, which not only compromises the privacy of individuals, but also hinders the healthy development of the medical industry. With the continuous improvement of cloud computing and big data technologies, the Internet of Things technology has been rapidly developed. RFID is one of the core technologies of the Internet of things. The application of RFID system to the medical system can effectively solve this problem of medical privacy. RFID tags in the system can collect useful information and conduct data exchange and processing with back-end server through the reader. The whole process of information interaction is mainly in the form of cipher text. In the context of the Internet of Things, the article presents a lightweight RFID medical privacy protection scheme. The scheme ensures security privacy of the collected data via secure authentication. The security analysis and evaluation about the scheme indicate that the protocol can effectively prevent the risk of medical privacy data being easily leaked.

Many artificial intelligence technologies have been applied for extracting useful information from massive industrial big data. However, the privacy issues are usually overlooked in many existing methods. In this article, we propose an anonymous and privacy-preserving federated learning scheme for the mining of industrial big data. We explored the effect of the proportion of shared parameters on the accuracy through experiments, and found that sharing partial parameters can almost achieve the accuracy of sharing all the parameters. On this basis, our proposed federated learning scheme reduces the privacy leakage by sharing fewer parameters between the server and each participant. Specifically, we leverage differential privacy on shared parameters with Gaussian mechanism to provide strict privacy preservation; the effect of different ε and δ on accuracy is tested; and we keep track of δ-when it reaches a certain threshold, training shall be stopped. What's more, we employ a proxy server as the middle layer between the server and all the participants to achieve anonymity of participants; it is worth noting that this can also reduce the communication burden on the federated learning server. Finally, we provide the security analysis and performance evaluations by comparing with other schemes.

Internet of Things (IoT) has been developed rapidly to make our life easier. In many IoT applications (e.g., smart homes, healthcare, etc.), all the IoT devices should be synchronized in time. However, some malicious nodes located in the IoT network can influence the time synchronization, which may interrupt the IoT system and lead to serious accidents. Therefore, it is critical and challenging to guarantee the accuracy and consistency of time during the time synchronization among all the IoT devices. In this paper, we propose a blockchain-based scheme to assure the security during time synchronization in IoT. Specifically, a publicly verifiable ledger is utilized to record and broadcast time, which can minimize many attacks from external environments. The use of multiple time sources can avoid the vulnerabilities caused by the centralized generation of accurate time. Moreover, the decentralized structure of this scheme has the advantage of adapting the changes of network topology. By employing an improved Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, time synchronization can be implemented efficiently. At last, the analysis results show that our proposed scheme can achieve the desired security with high efficiency.

With the rapid development of big data and Internet of things (IOT), the number of networking devices and data volume are increasing dramatically. Fog computing, which extends cloud computing to the edge of the network can effectively solve the bottleneck problems of data transmission and data storage. However, security and privacy challenges are also arising in the fog-cloud computing environment. Ciphertext-policy attribute-based encryption (CP-ABE) can be adopted to realize data access control in fog-cloud computing systems. In this paper, we propose a verifiable outsourced multi-authority access control scheme, named VO-MAACS. In our construction, most encryption and decryption computations are outsourced to fog devices and the computation results can be verified by using our verification method. Meanwhile, to address the revocation issue, we design an efficient user and attribute revocation method for it. Finally, analysis and simulation results show that our scheme is both secure and highly efficient.

A balanced substrate-integrated waveguide (SIW) bandpass filter (BPF) is designed and fabricated based on through-quartz-via (TQV) technologies in this article. Sixth-order filtering response centered at 41.4 GHz with equal source and load termination is achieved by the transformation of the Chebyshev polynomial. By exploiting cross-coupled resonators, the 3-dB fractional bandwidth (FBW) achieves 12.1%, and the out-of-band rejection is better than 30 dB with frequencies up to 2f₀. High CM suppression can be achieved up to 2.2f₀ with the help of two slots on the bottom metal layer. Based on the theory of the proposed SIW BPF, asymmetric diplexer with different resonance orders for two channels is studied with high isolation and wide stopband. The mixed dual-mode (TE₁₀₁ and TE₃₀₁) and single-mode (TE₁₀₁) junction resonators provide a transition between the input port and two channels. Diplexer with lower and upper channels centered at 20.2 and 37.5 GHz, respectively, is fabricated and measured to verify the proposed structure, which exhibits high isolation better than 40 dB over the whole band and a wide stopband up to 3f₀ of the lower channel. Measured results are in good agreement with the simulations.