Due to the explosive growth of electronic education records with highly sensitive nature, the security and authenticity of records have become an urgent issue to be addressed for data sharing. Although the blockchain-assisted searchable attribute-based encryption scheme provides certain trustworthiness for education records sharing, it still suffers from the risk of single key leakage and heavy computational overheads. In this paper, we propose a secure and efficient distributed sharing scheme with attribute-based searchable encryption (SEDS). On the one hand, we design a distributed public key searchable encryption method, which collaboratively generates distributed keys by decentralized blockchain nodes, effectively reducing the risk of single key leakage. On the other hand, we adapt and extend a fast pairing attribute-based encryption method to reduce the computational burden. We evaluate the performance of SEDS through theoretical analysis and experimental verification. The results show that SEDS can not only resist keyword guessing attacks but also reduce encryption and decryption time by 51% and 52% compared to traditional attribute-based encryption schemes, achieving a more secure and efficient education records sharing.

Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improved neighborhood knowledge capture. However, the focus on local interactions leads to imbalanced transmission of global topological information and increased risk of node-specific information being overwhelmed during aggregation due to the imbalance between fraud and benign nodes. In this paper, we first summarize the impact of topology and class imbalance on downstream tasks in GNN-based fraud detection, as the problem of imbalanced supervisory messages is caused by fraudsters' topological behavior obfuscation and identity feature concealment. Based on statistical validation, we propose a novel dual-view graph representation learning method to mitigate Message imbalance in Fraud Detection (MimbFD). Specifically, we design a topological message reachability module for high-quality node representation learning to penetrate fraudsters' camouflage and alleviate insufficient propagation. Then, we introduce a local confounding debiasing module to adjust node representations, enhancing the stable association between node representations and labels to balance the influence of different classes. Finally, we conducted experiments on three public fraud datasets, and the results demonstrate that MimbFD exhibits outstanding performance in fraud detection.

Blockchain-based federated learning (FL) technology enhances the performance of traditional FL and is widely applied in various fields, such as image recognition and medical diagnosis. To improve system security, numerous studies typically focus on threats like Byzantine attacks and privacy leakage. However, real-world data is often heterogeneous, and existing blockchain-based FL systems still suffer from: 1) Byzantine detection algorithms often perform poorly in heterogeneous data scenarios and 2) traditional noise addition methods to protect privacy may lead to reduced data availability. To address the above challenges, we propose SraFBS, a Byzantine-robust federated aggregation framework based on a blockchain system for heterogeneous data in decentralized scenario. Our framework introduces a role allocation mechanism, categorizing users to workers, a verification committee, and an aggregation committee. Workers use a key negotiation mechanism to create masks that can be offset during the aggregation phase, protecting privacy while reducing the impact of noise. The verification committee identifies byzantine attackers, and the aggregation committee ensures secure aggregation. Experimental results on image classification tasks show that our system effectively identifies 40% Byzantine attackers in heterogeneous data scenarios, achieving a successful attack rate below 4%, while maintaining model training accuracy close to attack-free FedAvg.

Federated Learning (FL) is a privacy-preserving machine learning paradigm, enabling decentralized devices to collaboratively train models without sharing local data. Traditional FL approaches, however, rely on averaging parameters across clients with homogeneous models, which limits their applicability in scenarios where clients require heterogeneous models. In this paper, we propose FedISMH, a novel approach to address model heterogeneity in FL. Instead of directly applying knowledge distillation, FedISMH clusters clients based on the structural similarities of client models, where clients’ structural features can be extracted through either labeled or unlabeled dataset This allows the proposed model to identify clients with similar model architectures while preserving privacy. Additionally, FedISMH introduces a dynamic mechanism to manage noise clients by aligning them with the most structurally similar clusters, ensuring that their inclusion promote the performance of the cluster. Experimental results on MNIST and SVHN demonstrate that FedISMH consistently outperforms state-of-the-art methods in both IID and Non-IID settings, offering improved accuracy, robustness, and flexibility in heterogeneous FL environments.

Sequential recommendation is a hot research in recommender systems, which Transformer-based models have achieved state-of-the-art performance. However, existing methods lack consideration of historical-level information, leading to ineffective modeling of user preference. To utilize history-level information and enhance item sequence representation infused with historical information, we design a model based on dual bi-directional Transformer encoders and dual Contrastive Learning named DBT4Rec. We first design a dual bi-directional Transformer encoder to capture the relationship between item-level sequences and history-level sequences, then design a dual Contrastive Learning to enhance item sequence representation integrated with history information. Finally, experiment results on three public benchmark datasets show that our model outperforms state-of-the-art models for sequential recommendation.

Sequential recommendations play a crucial role in modern recommender systems because they capture users’ dynamic interests based on his/her historical interactions. Despite the progress of existing methods in sequential recommendation, they only focus on modeling user interaction sequences but ignore multi-perspective sequences information when modeling user preferences, leading to ineffective modeling user preferences. In other words, these methods lack sufficient semantic information to effectively model the one, who considers multi-perspective factors when purchasing an item. To solve this problem, we propose a multi-perspective information and multi-task contrastive learning framework for sequential recommendation, named DIML. DIML can model user preferences by using multi-perspective information, and multi-task contrastive learning can alleviate data sparsity and enhance data quality. Specifically, we first design the information encoding layer, which enriches item semantic information from multi-perspective sequential information to model user preferences more precisely. Then we design a multi-task contrastive learning module to enhance the data quality of multi-perspective information. Finally, experiments on Taobao, JD and MovieLens datasets show that our model is better than the comparison baselines in terms of evaluation metric NDCG, HR and MRR.

Graph Contrastive Learning (GCL) has gained extensive attentions due to its success in label scarcity. GCL methods usually utilizes the graph neural network to learn node representation. However, the graph neural network can only aggregate direct neighbor features in each convolutional layer. The contextual dependencies and higher-order structural information among nodes can’t be captured by the local aggregation. In addition, existing GCL methods don’t consider semantic similarity when constructing positive and negative sample pairs of nodes. To address the problems, we propose a Higher-order Semantic-aware Adaptive Graph Contrastive Learning (HSAGCL) method. HSAGCL first extracts the semantic information of higher-order substructures of nodes, thereby integrating indirect neighbor features into node features. Then, HSAGCL adaptively generates augmented views of graphs through self-attention mechanism. The self-attention scores characterize the importance of correlation between nodes, providing a measure for the selection of positive and negative samples. Finally, HSAGCL learn effective graph-level discriminative representations for graph classification by jointly optimizing the contrastive loss and classification loss. Extensive experiments on 5 benchmark datasets show that the proposed HSAGCL achieves significant performance improvements in graph classification, with an average accuracy improvement of 8.94% over state-of-the-art methods.

Abstract Knowledge graph (KG) is introduced as side information into recommender systems, which can alleviate the sparsity and cold start problems in collaborative filtering. Existing studies mainly focus on modeling users’ historical behavior data and KG-based propagation. However, they have the limitation of ignoring noise information during recommendation. We consider that noise exists in two parts (i.e. KG and user-item interaction data). In this paper, we propose Knowledge-aware Dual-Channel Graph Neural Networks (KDGNN) to improve the recommendation performance by reducing the noise in the recommendation process. Specifically, (1) for the noise in KG, we design a personalized gating mechanism, namely dual-channel balancing mechanism, to block the propagation of redundant information in KG. (2) For the noise in user-item interaction data, we integrate personalized and knowledge-aware signals to capture user preferences fully and use personalized knowledge-aware attention to denoise user-item interaction data. Compared with existing KG-based methods, we aim to propose a knowledge-aware recommendation method from a new perspective of denoising. We perform performance analysis on three real-world datasets, and experiment results demonstrate that KDGNN achieves strongly competitive performance compared with several compelling state-of-the-art baselines.

A blockchain provides decentralization and trustlessness features for the Industrial Internet of Things (IIoT), which expands the application scenarios of IIoT. To address the problem that blockchains cannot actively obtain off-chain data, the blockchain oracle is proposed as a bridge between the blockchain and external data. However, the existing oracle schemes make it difficult to solve the problem of low quality of service caused by frequent data changes and heterogeneous devices in IIoT, and the current oracle node selection schemes are difficult to balance security and quality of service. To tackle these problems, this paper proposes a secure and reliable oracle scheme that can obtain high-quality off-chain data. Specifically, we first design an oracle node selection algorithm based on a Verifiable Random Function (VRF) and reputation mechanism to securely select high-quality nodes. Second, we propose a data filtering algorithm based on a sliding window to further improve the consistency of the collected data. We verify the security of the proposed scheme through security analysis. The experimental results show that the proposed scheme can effectively select high-quality nodes, reduce data differences, and improve the quality of service of the oracle. In the oracle network with malicious nodes accounting for 10%, the data accuracy rate is increased by about 4%, and the data variance is reduced by about 45% on average.

Due to the explosion of online educational resources, the protection and management of educational multimedia data have become more challenging. Blockchain has emerged as a promising technology for copyright management due to its decentralized and traceable nature. However, it still faces problems such as difficulties in copyright maintenance and delays in the consensus process, especially for educational multimedia data with large amounts of data, high demand for privacy, and many participants. This paper proposes a secure and trustworthy copyright protection method based on a redactable blockchain to address these issues. We use a decentralized chameleon hash function for copyright maintenance to enable trusted blockchain editing. This can promptly modify infringing copyrights to complete efficient copyright maintenance. For the delay during consensus, we design a consensus mechanism called Proof of Behavior (PoB) based on the Bayesian network (BN), which can achieve fast and reliable consensus by predicting user behavior and selecting highly trusted nodes to participate in consensus. Finally, We conduct simulation experiments to validate the performance of our proposed approach. Specifically, our approach effectively reduces storage space by 5%-10% comparable to other blockchain solutions and enhances system scalability and security.

Knowledge graph (KG) as auxiliary information can solve the cold-start and data sparsity problems of recommender systems.However, most existing KG-based recommendation methods focus on how to effectively encode items with that users have interacted into entities and propagate them explicitly, but neglect the relation-level and context-level modeling of collaborative signals.Therefore, it is inevitable to incorporate some unrelated entities while utilizing a propagation strategy, which may weaken part of the recommendation performance.To address this problem, we propose a novel method named Collaborative Relation Context Consistency (CRCC).Compared with other KG-based methods, we model the relation-level and context-level of collaborative signals in a fine-grained manner.Specifically, we segment the user's collaborative knowledge graph to learn related entity information separately to enrich the embedding of users.Moreover, CRCC links the consistency score between the items that users and neighbors have interacted with as the fusion basis, and then we consider the inherent popularity of items while incorporating consistent entities to enhance the embedding representation of items.Extensive experiments on three real-world datasets show that CRCC outperforms several compelling baselines in both CTR prediction and top-K recommendation.

Set-valued data and social network provide opportunities to mine useful, yet potentially security-sensitive, information. While there are mechanisms to anonymize data and protect the privacy separately in set-valued data and in social network, the existing approaches in data privacy do not address the privacy issue which emerge when publishing set-valued data and its correlative social network simultaneously. In this paper, we propose a privacy attack model based on linking the set-valued data and the social network topology information and a novel technique to defend against such attack to protect the individual privacy. To improve data utility and the practicality of our scheme, we use local generalization and partial suppression to make set-valued data satisfy the grouped ρ-uncertainty model and to reduce the impact on the community structure of the social network when anonymizing the social network. Experiments on real-life data sets show that our method outperforms the existing mechanisms in data privacy and, more specifically, that it provides greater data utility while having less impact on the community structure of social networks.

Transactional data with attributes of multiple types may be extremely useful to secondary analysis (e.g., learning models and finding patterns). However, anonymization of such data is challenging because it contains multiple types of attributes (e.g., relational and set-valued attributes). Existing privacy-preserving techniques are not applicable to address this problem. In this paper, we propose a novel graph-based multifold model to anonymize data with attributes of multiple types. Under this model, such data are modelled as a graph, and multifold privacy is guaranteed through fuzzing on sensitive attributes and converting associations among items into an uncertain form. Specifically, we define a multi-objective attack model in a graph and devise a safety parameter and algorithm to prevent such attacks. Experiments have been performed on real-life data sets to evaluate the performance.

Data collection by social networking applications offers many opportunities for mining information, which provides a better understanding of social structures and their dynamic structures. Anonymization of social networks before they are published or shared is particularly important, since social network data usually contain much sensitive information on individuals. In this paper, we address the privacy problems of dynamic releases of social networks. We re-define the label-neighborhood attack model in dynamic social network releases. An adversary can use one-hop neighbor's network structure and label as background knowledge to identity the victim to learn more sensitive information. We propose a dynamic-l-diversity anonymized method to resist attacks. Experiments show that the proposed approach can retain much of the characteristics of the network while providing high utility.

With the popularity of social networks, privacy issues with regard to publishing social network data have gained intensive focus from academia. We analyzed the current privacy-preserving techniques for publishing social network data and defined a privacy-preserving model with privacy guarantee [Formula: see text]. With our definitions, the existing privacy-preserving methods, [Formula: see text]-anonymity and randomization can be combined together to protect data privacy. We also considered the privacy threat with label information and modify the [Formula: see text]-anonymity technique of tabular data to protect the published data from being attacked by the combination of two types of background knowledge, the structural and label knowledge. We devised a partial [Formula: see text]-anonymity algorithm and implemented it in Python and open source packages. We compared the algorithm with related [Formula: see text]-anonymity and random techniques on three real-world datasets. The experimental results show that the partial [Formula: see text]-anonymity algorithm preserves more data utilities than the [Formula: see text]-anonymity and randomization algorithms.

Driven by mutual benefits, there is a demand for transactional data sharing among organizations or parties for research or business analysis purpose. It becomes an essential concern to provide privacy-preserving data sharing and meanwhile maintain data utility, due to the fact that transactional data may contain sensitive personal information. Existing privacy-preserving methods, such as k-anonymity and l-diversity, cannot handle high-dimensional sparse data well, since they would bring about much data distortion in the anonymization process. In this paper, we use bipartite graphs with node attributes to model high-dimensional sparse data, and then propose a privacy-preserving approach for sharing transactional data in a new vision, in which the bipartite graph is anonymized into a weighted bipartite graph by clustering node attributes. Our approach can maintain privacy of the associations between entities and resist certain attackers with knowledge of partial items. Experiments have been performed on real-life data sets to measure the information loss and the accuracy of answering aggregate queries. Experimental results show that the approach improves the balance of performance between privacy protection and data utility.