While generative models for tabular data offer strong capabilities in data synthesis, they also raise serious privacy concerns. Property Proportion Inference Attacks (PPIAs) pose a critical threat by aiming to infer the distribution of sensitive attributes in the training data. Existing approaches often struggle with low efficiency and limited adaptability in high-dimensional tabular settings.In this paper, we propose PPIA-MTL, a novel attack framework based on Multi-Task Learning (MTL) that enables efficient parallel inference of multiple attributes, significantly reducing computational cost. We further extend the framework to support continuous attributes and introduce a unified evaluation metric, CACRS (Continuous Attribute Comprehensive Reasoning Score), which comprehensively assesses inference performance from the perspectives of distributional consistency, numerical error, and more.Experiments on real-world datasets show that PPIA-MTL achieves a minimum MAE of 1.55% on binary attributes and improves inference accuracy for continuous attributes by up to 15.5 over existing methods. As the number of inference tasks increases, training cost is reduced by more than 10×. Finally, we apply PPIA-MTL as a privacy auditing tool and find that some diffusion models exhibit lower inference risk while maintaining high utility, demonstrating promising potential for balancing privacy and utility under the Group-Level Statistical Privacy Risk (GSPR).

In zero-trust environments, fair exchange schemes have long faced challenges of low efficiency and high computational overhead when verifying the integrity of large-scale data. To address these issues, this paper proposes ZKFDT, an efficient data fair exchange scheme based on optimized zero-knowledge proof algorithms, offering improvements in efficiency, fairness, and security. In terms of efficiency, ZKFDT leverages IPFS’s hash-based addressing mechanism to significantly reduce network communication overhead compared to traditional data transmission methods, while also optimizing the multi-scalar multiplication algorithm, improving proof generation efficiency by 2x. Regarding security, ZKFDT adopts the more secure ABR23 protocol, addressing the malleability attack vulnerabilities of Groth16 while maintaining its low communication overhead. Through the implementation of smart contracts, including proof verification, atomic swaps, and time-lock functionality, ZKFDT ensures fairness and immutability in data transactions. Experimental results show that ZKFDT demonstrates high efficiency and practical feasibility in large-scale data transaction applications.

The Domain Name System (DNS) plays a critical role in the Internet, making it a popular target for cyber attackers. Malicious actors use DNS to locate their command and control servers, and spam often contains URLs linked to domains that host malicious servers. Detecting such malicious domain activities is essential. While many prior works have shown promising results in detecting malicious domains, the time and storage required during detection are relatively high.In this paper, we propose a lightweight and effective malicious domain detection system called Quicksolver, ideal for large-scale networks. Our system uses only domain features, eliminating the need for additional costs associated with DNS traffic and registration information. Additionally, we use an improved autoencoder as our classifier, combining it with neural networks to avoid the need for setting and adjusting thresholds manually.We evaluated Quicksolver using malicious data collected from a certain ISP over three months. The results show that Quicksolver has better detection ability and lower detection time compared to other state-of-the-art methods. Furthermore, it can automatically identify unknown malicious domains that are misused in seven types of cyber attacks.

As for a pipe-line plant subjected to complex logical control specifications, an approach is proposed to generate the control codes via Petri net such that the plant is run as concurrently as possible. A PN model, called the plant net, is designed to represent the whole process of a pipe-line system. In this plant net, the tasks, including fluid transportation from one tank to another and cleaning tank, are represented by operational places, and level sensors in tanks are modeled by labels assigned with transitions. Further, conflict structures are used to describe the conflict relations among tasks due to the shared valves and pipes. By introducing a priority order in the transitions, an algorithm is presented to compute the control commands for valves based on this plant net. A beer filtration plant is taken as an example to illustrate the method.