Reversible data hiding techniques have garnered increasing scholarly attention for their capacity to generate high-fidelity watermarked images while enabling lossless restoration of the original images. Pixel value ordering (PVO) stands as a pivotal reversible data embedding approach that adeptly leverages pixel sequencing within an image block to facilitate high-fidelity image embedding and reversible retrieval. The efficacy of PVO-based methodologies hinges on the block size and classification thresholds. Nonetheless, many techniques ascertain the appropriate parameters through exhaustive searches within confined search spaces, demanding significant computational time. This paper introduces a novel pixel value ordering method based on differential evolution (PVO-DE) to address these challenges more effectively. PVO-DE dynamically identifies the optimal or suitable block size and threshold based on distinct images and embedding quantities, thereby optimizing image utilization and reducing time expenditures. Furthermore, a complexity algorithm, fused with the differential evolution algorithm, is proposed to harness the strengths of diverse complexity algorithms and pinpoint the algorithm most conducive to the current image for optimal performance. Results demonstrate a reduction in computational costs for determining the optimal parameter set, alongside enhanced performance of marked images compared to prior approaches.

Side-channel attacks (SCAs) serve as a fundamental tool for evaluating the implementation security of cryptographic devices. In real-world acquisition scenarios, however, power consumption traces are frequently degraded by device clock jitter and external noise interference, resulting in pronounced temporal misalignment and signal distortion. As a result, the efficiency and stability of attack convergence are seriously restricted. To address this, we propose a correlation power analysis (CPA) framework that integrates successive variational mode decomposition (SVMD) and peak-anchor-based alignment (PA-CPA). Firstly, the method uses SVMD to adaptively decompose and reconstruct the original power consumption traces, effectively suppressing random interference and preserving leakage-relevant features. A robust anchor point sequence is then constructed and global linear resampling and local dynamic time warping (DTW) are combined to realise the segmental fine alignment of the power consumption traces. This improves feature synchronisation and alignment accuracy. Experimental results demonstrate that the proposed method achieves higher attack success rates and faster convergence.

Users outsource images to edge servers physically closer to their location for real time applications because of the low latency and low transmission overhead. Outsourcing to these edge servers however, increases the risks to data privacy. Almost all existing privacy preserving image retrieval schemes utilize a single cloud server to execute retrieval tasks and provide centralized image retrieval but at high computational costs, thus are not suitable for the distributed edge environments with limited computing resources. We propose a lightweight privacy-preserving multi-source image retrieval scheme adapted specifically for the distributed edge environment. We apply high efficiency orthogonal decomposition and learning with errors (LWE) strategy to encrypt image features and construct cipher indexes and trapdoors, guaranteeing the security of the data, while reducing computational costs. The orthogonality of data ensures that the accuracy of retrieval results is not compromised by the random numbers used in the scheme. In addition, the proxy re-encryption technology is adopted to support the retrieval of multi-source images encrypted by unique data owners with different keys. A detailed performance analysis and comprehensive experiments demonstrate that our scheme guarantees data security with very high retrieval accuracy and a low computational burden, consistent with the demands of edge environments.