Weidong Fang

The rapid proliferation of the Internet of Things (IoT) leaves terminal devices vulnerable to considerable security challenges, notably the absence of robust yet efficient identity authentication mechanisms. Traditional certificate-based approaches incur substantial management overhead and storage expenditure, whereas Identity-Based Cryptography poses inherent key escrow risks. To tackle these challenges, this paper proposes a PUF and SM2-based certificateless identity authentication mechanism that integrates SM2 Certificateless Public Key Cryptography (a Chinese national cryptographic standard) with Physical Unclonable Functions (PUFs). Initially, the proposed solution utilizes PUF technology to derive a unique hardware-generated “fingerprint” from an IoT device, which functions as a root key to generate a partial user private key. This approach essentially binds the terminal’s identity to its physical hardware, thereby effectively mitigating physical cloning attacks against nodes. Moreover, through the adoption of a Certificateless Public Key Cryptography (CLPKC) framework, the complete user private key is jointly generated by a semi-trusted Key Generation Centre (KGC) and the terminal device itself. The comprehensive security analysis proves that the proposed scheme is provably secure under the random oracle model, capable of resisting various common attacks such as physical cloning, man-in-the-middle, and replay attacks. Performance evaluation confirms that the implemented PUF + SM2 certificateless mechanism significantly reduces the size of user public key identifiers to within 64 bytes, offering a substantial advantage over the 1–2 KB certificates typically required in conventional PKI/CA systems, thereby enhancing efficiency in storage and communication.

With the rapid development of information and communication technologies, the Internet of Things (IoT) has gradually become a key infrastructure supporting the development of the digital society

The integrity of electronic health record (EHR) is susceptible to compromise by hardware failures, software errors, or human errors. To date, numerous data integrity verification schemes have been proposed, but most face challenges related to third-party auditing and communication overhead. To address this, a novel EHR integrity verification scheme based on a multi-branch authentication tree is presented in this paper. By integrating an edge-based batch processing mechanism with data identity labeling technology, a low-overhead data verification framework is constructed, effectively reducing communication load. A minimal multi-branch tree structure is innovatively designed to enable parallel authentication and batch signing of data blocks. Concurrently, a random security code generation algorithm is introduced to ensure data security. Experimental and analytical results demonstrate that the proposed scheme maintains correctness, efficiency, and security, consistently achieving 100 % precision in detecting corrupted EHR data replicas. This scheme provides an efficient and reliable data integrity guarantee mechanism for EHR within edge computing environments and contributes significantly to building a trustworthy medical service system.

The beat signal obtained from frequency-modulated continuous-wave (FMCW) radar is a waveform that is corrupted by noise and requires filtering out interference components for frequency calibration. Traditional FFT methods are affected by the fence effect and spectral leakage, leading to a reduction in frequency estimation accuracy. Therefore, an improved double-spectrum-line interpolation frequency estimation algorithm is proposed in this paper, utilizing spectral refinement and phase interpolation. Firstly, the post-FFT spectral signal is refined to narrow the frequency search range and enhance frequency resolution, thereby separating the noise signal. Then, a frequency deviation factor is defined based on the relationship between adjacent phase angles. Finally, the signal’s phase angles are interpolated using the frequency deviation factor to estimate the frequency of the beat signal. Experimental results demonstrate that the proposed algorithm reduces the impact of quantization on the frequency distribution and increases the signal’s noise resistance. The proposed algorithm has a higher accuracy and lower standard deviation compared to the recently proposed algorithm.