Auditing differential privacy (DP) guarantees often relies on querying trained models with specially crafted queries, such as canaries, examples differing between two neighboring datasets. However, in this work, we revisit this common approach and identify a fundamental limitation: canary-based queries may not capture the strongest privacy leakage, as the most informative queries can shift during the training process. This mismatch can result in loose lower bounds on the privacy parameter$\varepsilon$, underestimating potential risks from query-based adversaries. To address this issue, we propose two methods. First, we introduce a consistent and optimizable surrogate privacy loss function that better aligns with the true privacy loss, called Privacy-loss Maximization Method (PMM), enabling systematic discovery of stronger queries through optimization. Second, we analyze how the optimal queries evolve with model training and propose a gradient-aligned query generation algorithm, called Gradient-Guided Querying (GGQ), that rapidly identifies high-risk queries by aligning their gradients with the distribution of model parameters. Empirical evaluations across multiple tasks demonstrate that our methods consistently produce stronger privacy audit results, offering a more accurate assessment of the privacy risks associated with training algorithms.

The widespread deployment of smart grids has brought significant convenience to residential life. However, it also presents key challenges for data aggregation in smart grids.M1: The hierarchical structure of smart grid consumers (e.g., residential, industrial, commercial) requires differentiated allocation strategies to meet varying electricity demands while protecting consumer privacy.M2: The existing methods, such as superincreasing sequence, often face efficiency challenges, particularly when dealing with multidimensional data.M3: Smart meters continuously collect diverse power consumption data containing users' private information, which is vulnerable to tampering or loss, compromising data integrity and impacting power dispatch decisions. To address these challenges, this paper proposes a blockchain-based, efficient, verifiable, and weighted multidimensional data aggregation scheme for smart grids. First, a novel five-layer cloud-chain-assisted multiscenario data security aggregation model is proposed. Second, instead of using superincreasing sequences, we introduce the Chinese Remainder Theorem to process multidimensional data, thereby reducing communication complexity. Additionally, the property of quadratic reciprocity is leveraged to enhance the decryption method of the Paillier cryptosystem, reducing computational overhead. A weighted aggregation function is implemented to accurately aggregate data based on different user attributes. Furthermore, we propose two sample configurations to address distinct scenario requirements. Security analysis and experimental results demonstrate that the proposed scheme meets practical requirements in terms of both security and efficiency.

Reliable protocol knowledge is often difficult to obtain in industrial networks, as industrial communications come with limited documentation, vendor-specific encodings, and opaque payloads. This lack of transparency hinders message interpretation and protocol analysis. To recover this missing protocol knowledge, network-trace-based protocol reverse engineering (PRE) infers message structure, field roles, and interaction logic directly from recorded traces. This enables protocol-aware intrusion detection, process monitoring, and protocol testing and fuzzing without access to device internals. Although PRE has advanced rapidly, existing techniques are developed under diverse objectives and assumptions. As a result, it is often unclear how isolated results relate to an end-to-end reverse-engineering workflow, and how evaluation outcomes should be compared across tasks and protocols. In this article, we cast reverse engineering of industrial protocols from network traces as a task-driven pipeline and articulate a unified task decomposition spanning message type identification, protocol syntax and semantic inference, payload pattern recognition and semantic inference, and protocol state machine reconstruction. For each task, we describe key methodological themes, common evaluation practices, and practical limitations that affect robustness and deployability in industrial settings. We further discuss security, privacy, and ethical risks that accompany increasingly capable PRE, and identify promising research directions toward more systematic, dependable, and deployment-oriented PRE methodologies.

Polarimetric Synthetic Aperture Radar (PolSAR) images have emerged as an important data source for land cover classification research due to their all‐weather, all‐day monitoring capabilities. Deep learning‐based classification methods have recently gained significant attention in PolSAR image classification since they have demonstrated excellent performance in the computer vision field. However, the main issue with deep learning‐based methods is that they require large amounts of training data. Additionally, the scarcity of labeled data is a significant challenge in the PolSAR image field. Therefore, in this article, we proposed an advanced semisupervised deep self‐training algorithm for PolSAR image classification, which utilized both labeled and unlabeled data in a semisupervised way. Then, a training optimization method and a high‐confidence sample selection strategy are proposed by integrating consistency regularization. In addition, to achieve stronger feature extraction capabilities, we designed a deep learning‐based classifier that combines residual blocks with an efficient multiscale attention module. We have conducted experiments on three popular real PolSAR datasets: 1989 Flevoland, 1991 Flevoland, and Oberpfaffenhofen. The classification results on these datasets demonstrated that the proposed method outperforms several other comparison algorithms, with overall accuracy up to 99.3%, 99.15%, and 94.12%, respectively. These results demonstrated the effectiveness of the proposed method for PolSAR image classification.