The trading of data is becoming increasingly important as it holds substantial value. A blockchain-based data marketplace can provide a secure and transparent platform for data exchange. To facilitate this, developing a fair data exchange protocol for digital goods has garnered considerable attention in recent decades. The Zero Knowledge Contingent Payment (ZKCP) protocol enables trustless fair exchanges with the aid of blockchain and zero-knowledge proofs. However, applying this protocol in a practical data marketplace is not trivial.In this paper, several potential attacks are identified when applying the ZKCP protocol in a practical public data marketplace. To address these issues, we propose SmartZKCP, an enhanced solution that offers improved security measures and increased performance. The protocol is formalized to ensure fairness and secure against potential attacks. Moreover, SmartZKCP offers efficiency optimizations and minimized communication costs. Evaluation results show that SmartZKCP is both practical and efficient, making it applicable in a data exchange marketplace.

Multiparty private set intersection (mPSI) protocol is capable of finding the intersection of multiple sets securely without revealing any other information. However, its limitation lies in processing only those elements present in every participant's set, which proves inadequate in scenarios where certain elements are common to several, but not all, sets.

How to effectively identify vehicles in SAR (synthetic aperture radar) images is a challenging problem in the SAR image understanding community. Compared with the bounding-box-based localization by object detection networks, object segmentation could provide the complete description of vehicles, which not only detects vehicles’ positions but also obtains their contours. However, vehicle segmentation in SAR images is a hard task due to speckle noise. To address this problem, this letter proposes a focal-pyramid-based network for vehicle segmentation in SAR images, called FVSNet, consisting of a focal pyramid module and a segmentation module. The focal pyramid module is built for extracting the multi-scale semantic feature maps from inputted SAR images, where the channel weights of the feature maps from backbone are automatically adjusted via a channel attention and the ambiguous features derived from speckle noise are eliminated via a designed mask-based cross attention. The segmentation module is designed to enlarge the feature maps outputted from the focal pyramid module with higher-resolution feature maps to obtain vehicle segmentation results. In addition, considering that there exists no open-source manually annotated dataset for handling the SAR vehicle segmentation task, we build and release a SAR vehicle-segmentation dataset based on the public MSTAR dataset. Experimental results on this dataset demonstrate the effectiveness of the proposed network in comparison with eight existing networks.