Two‐party private set intersection (PSI) plays a pivotal role in secure two‐party computation protocols. The communication cost in a PSI protocol is normally influenced by the sizes of the participating parties. However, for parties with unbalanced sets, the communication costs of existing protocols mainly depend on the size of the larger set, leading to high communication cost. In this paper, we propose a low communication‐cost PSI protocol designed specifically for unbalanced two‐party private sets, aiming to enhance the efficiency of communication. For each item in the smaller set, the receiver queries whether it belongs to the larger set, such that the communication cost depends solely on the smaller set. The queries are implemented by private information retrieval which is constructed with trapdoor hash function. Our investigation indicates that in each instance of invoking the trapdoor hash function, the receiver is required to transmit both a hash key and an encoding key to the sender, thus incurring significant communication cost. In order to address this concern, we propose the utilization of a seed hash key, a seed encoding key, and a Latin square. By employing these components, the sender can autonomously generate all the necessary hash keys and encoding keys, obviating the multiple transmissions of such keys. The proposed protocol is provably secure against a semihonest adversary under the Decisional Diffie–Hellman assumption. Through implementation demonstration, we showcase that when the sizes of the two sets are 2 8 and 2 14 , the communication cost of our protocol is only 3.3% of the state‐of‐the‐art protocol and under 100 Kbps bandwidth, we achieve 1.46x speedup compared to the state‐of‐the‐art protocol. Our source code is available on GitHub: https://github.com/TAN-OpenLab/Unbanlanced-PSI .

Recently rumors have been rapidly propagated while the Internet has been extensively developed. Research shows that highly credible comments with a distinct stance have worthy information. In this paper, we attempt to combine user credibility and user stance to capture worthy comments during the information-dissemination process to detect rumors. We propose a User Stance Bi-Directional Graph Attention Networks (USBGAT) model to extract accurate information for rumor detection based on high credibility users with strong stance, and diminish ineffectively neutral comments. Specifically, we take user features and user stance as a component of the node features, with multiviews features of tweets content engaged. Then, we use bidirectional graph attention networks (GAT) to capture the high-level representation of the rumor. Furthermore, we reweight the node features according to users' stances. Extensive experiments on two datasets: Pheme and Weibo show that our model is superior to the state-of-the-art models, especially in the early rumor detection. Our code and data are available at https://github.com/TAN-OpenLab/USB-GAT

The research on detecting violent behavior in videos has made good progress, which provides good support for monitoring abnormal videos spread in the network, so as to achieve the effect of purifying the network space environment. A large number of current violence detection models have achieved good performance in experimental environments, but their generalization ability is insufficient. Violent behavior often occurs in a variety of scenarios, automatic detection of violent behavior requires a model with strong generalization. In this paper, a crowd violence behavior detection model with good generalization ability based on human contour and dynamic characteristics was designed. The model generalization ability is improved by focusing on the human features in the video and using the human dynamic features obtained from adjacent frames. In our model, a 3D-CNN framework was used to extract spatial features of the input feature map, and LSTM was used to fuse the temporal feature, we call this model HD-Net. Through multiple contrast experiments, the generalization ability of HD-Net is tested on three datasets: RLVS, Hockey and violent flow. Comparing with other classical violence detection models, the good generalization ability of the model is verified.