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Collaborative Filtering has achieved great success in capturing users’ preferences over items. However, existing techniques only consider limited collaborative signals, leading to unsatisfactory results when the user-item interactions are sparse. In this paper, we propose a Cross-grained Neural Collaborative Filtering model (CNCF), which enables recommendation more accurate and explainable. Specifically, we first construct four kinds of interaction graphs to model both fine-grained collaborative signals and coarse-grained collaborative signals, which can better compensate for the high sparsity of user-item interactions. Then we propose a fine-grained collaborative representation learning and design Light Attribute Prediction Networks ( $LAPN$ ) to capture the high-order attribute interactions and enhance the prediction accuracy. Finally, we propose a coarse-grained collaborative representation learning to represent user preferences based on diverse latent intent factors. The experiments demonstrate the high effectiveness of our proposed model.

Abstract An accurate short-term load forecasting plays an important role in modern power system’s operation and economic development. However, short-term load forecasting is affected by multiple factors, and due to the complexity of the relationships between factors, the graph structure in this task is unknown. On the other hand, existing methods do not fully aggregating data information through the inherent relationships between various factors. In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC. GLFN-TC uses the graph learning module to automatically learn the relationships between variables to solve problem with unknown graph structure. GLFN-TC effectively handles temporal and spatial dependencies through two modules. In temporal convolution module, GLFN-TC uses dilated 1D-CNN to extract temporal dependencies from historical data of each node. In densely connected residual convolution module, in order to ensure that data information is not lost, GLFN-TC uses the graph convolution of densely connected residual to make full use of the data information of each graph convolution layer. Finally, the predicted values are obtained through the load forecasting module. We conducted five studies to verify the outperformance of GLFN-TC. In short-term load forecasting, using MSE as an example, the experimental results of GLFN-TC decreased by 0.0396, 0.0137, 0.0358, 0.0213 and 0.0337 compared to the optimal baseline method on ISO-NE, AT, AP, SH and NCENT datasets, respectively. Results show that GLFN-TC can achieve higher prediction accuracy than the existing common methods.

With the world’s data volume growing exponentially, it becomes critical to link it and make decisions. Privacy-preserving record linkage (PPRL) aims to identify all the record information corresponding to the same entity from multiple data sources, without disclosing sensitive information. Previous works on multi-party PPRL methods typically adopt homomorphic encryption technology due to its ability to perform computations on encrypted data without needing to decrypt it first, thus maintaining data confidentiality. However, these methods have notable shortcomings, such as the risk of collusion among participants leading to the potential disclosure of private keys, high computational costs, and decreased efficiency. The advent of trusted execution environments (TEEs) offers a solution by protecting computations involving private data through hardware isolation, thereby eliminating reliance on trusted third parties, preventing malicious collusion, and improving efficiency. Nevertheless, TEEs are vulnerable to side-channel attacks. In this work, we propose an enhanced PPRL method based on TEE technology. Our methodology involves processing plaintext data within a TEE using the inner product mask technique, which effectively obfuscates the data, making it impervious to side-channel attacks. The experimental results demonstrate that our approach not only significantly improves resistance to side-channel attacks but also enhances efficiency, showing better performance and privacy preservation compared to existing methods. This work provides a robust solution to the challenges faced by current PPRL methods and sets the stage for future research aimed at further enhancing scalability and security.

With the advent of the big data era, data security and sharing have become the core elements of new-era data processing. Privacy-preserving record linkage (PPRL), as a method capable of accurately and securely matching and sharing the same entity across multiple data sources, is receiving increasing attention. Among the existing research methods, although PPRL methods based on Bloom Filter encoding excel in computational efficiency, they are susceptible to privacy attacks, and the security risks they face cannot be ignored. To balance the contradiction between security and computational efficiency, we propose a multi-party PPRL method based on secondary encoding. This method, based on Bloom Filter encoding, generates secondary encoding according to well-designed encoding rules and utilizes the proposed linking rules for secure matching. Owing to its excellent encoding and linking rules, this method successfully addresses the balance between security and computational efficiency. The experimental results clearly show that, in comparison to the original Bloom Filter encoding, this method has nearly equivalent computational efficiency and linkage quality. The proposed rules can effectively prevent the re-identification problem in Bloom Filter encoding (proven). Compared to existing privacy-preserving record linkage methods, this method shows higher security, making it more suitable for various practical application scenarios. The introduction of this method is of great significance for promoting the widespread application of privacy-preserving record linkage technology.

With the continuous development of blockchain technology, many blockchain applications, such as digital currencies in the form of tokens, are deployed. However, due to the lack of data and value inter-blockchain transmission methods, these chain applications form many data islands. Therefore, cross-chain technology is proposed and applied in distributed transaction platforms, digital finance, e-government and many other different fields. Although cross-chain technology just started, the cross-chain technology and its applications are gradually improving to meet the development needs of blockchain system. At the same time, they have already realized asset transfers across heterogeneous blockchains and platforms, and some projects have realized cross-chain information exchange. At present, cross-chain technology includes the following typical methods: Hash-locking, Notary scheme, Distributed private key control, Sidechains/Relays and Tight coupling. This paper analyzes the above methods from the aspect of techniques, security requirements, and technical limitation, and compares their applicability. With public blockchains and consortium blockchains, this paper analyzes their problems and challenges in cross-chain technology and introduces the main approaches of existing solutions. By analyzing the existing cross-chain technologies and problems, this paper further introduces eight popular cross-chain projects and their application prospects. Finally, this paper summarizes and proposes the future research topics and development trends of cross-chain technology.

Entity matching (EM) is a fundamental problem in data preprocessing, and is a long running topic in big data analytics and mining communities. In big data era, (nearly) real-time data applications become popular, and call for progressive EM, which produces as many match pairs as possible in very limited time. Previous progressive EM focus on memory based solutions, but disk based solutions are necessary when dirty datasets cannot be fully loaded into memory. To this end, we propose a cost benefit analysis based progressive EM approach, which partitions data according to coarse clustering results and then iteratively schedules data partitions in a greedy way for high progressive resolution. At first, based on estimated record pair similarities, records are fast coarsely clustered; then, record clusters with near average similarities are greedily distributed to the same partitions, and data partitions are cached. After that, cost model is defined with time and space constrains, and benefit model is defined with expected resolution results. On the basis of the cost benefit model, a greedy approximate method is proposed to effectively schedule data for high progressiveness of EM. Finally, we implement extensive experiments over several datasets to evaluate our approach, and show its advantages over existing works.

People often use online social networks not only to express attitudes towards others, but also to make decisions, which forms signed heterogeneous information networks. Both sign prediction and behavior prediction can provide useful information for networks analysis, each of which has been a hot topic. However, existing methods for sign prediction mainly rely on the features from labeled links but ignore users' behavior and the features from unlabeled links, which often leads to dumb results. Similarly, inferring users' behavior without considering links' signs is dull as well. In order to solve this issue, in this paper, we present a novel model called SPBP to integrate Sign Prediction with Behavior Prediction in the context of signed heterogeneous information networks. It simultaneously captures users' social links (including both labeled links and unlabeled links) and users' behavior to improve the accuracy of prediction. First, due to the lack of labeled links in main stream social networks, we propose correlation estimation methods to estimate social correlation and behavioral correlation between users respectively. Then we encode structural balance-based features and status-based features according to social psychology theories. With the extracted features, we propose a sign prediction algorithm based on transfer learning to use knowledge extracted from related source networks to train the target network, which can effectively make up for the incompleteness of target samples. Finally, we propose a behavior prediction algorithm based on the predicted signs of links. Extensive experiments conducted on real-world signed heterogeneous information networks, Epinions, Slashdot and Wiki-RfA, demonstrate that SPBP can effectively solve both the sign prediction problem and the behavior problem.

A lot of scholars have focused on developing effective techniques for package queries, and a lot of excellent approaches have been proposed. Unfortunately, most of the existing methods focus on a small volume of data. The rapid increase in data volume means that traditional methods of package queries find it difficult to meet the increasing requirements. To solve this problem, a novel optimization method of package queries (HPPQ) is proposed in this paper. First, the data is preprocessed into regions. Data preprocessing segments the dataset into multiple subsets and the centroid of the subsets is used for package queries, this effectively reduces the volume of candidate results. Furthermore, an efficient heuristic algorithm is proposed (namely IPOL-HS) based on the preprocessing results. This improves the quality of the candidate results in the iterative stage and improves the convergence rate of the heuristic algorithm. Finally, a strategy called HPR is proposed, which relies on a greedy algorithm and parallel processing to accelerate the rate of query. The experimental results show that our method can significantly reduce time consumption compared with existing methods.

Nowadays, people usually participate in multiple social networks simultaneously, e.g., Facebook and Twitter. Formally, the correspondences of the accounts that belong to the same user are defined as anchor links, and the networks aligned by anchor links can be denoted as aligned networks. In this paper, we study the problem of anchor link prediction (ALP) across a pair of aligned networks based on social network structure. First, three similarity metrics (CPS, CCS, and CPS+) are proposed. Different from the previous works, we focus on the theoretical guarantees of our metrics. We prove mathematically that the node pair with the maximum CPS or CPS+ should be an anchor link with high probability and a correctly predicted anchor link must have a high value of CCS. Second, using the CPS+ and CCS, we present a two-stage iterative algorithm CPCC to solve the problem of the ALP. More specifically, we present an early termination strategy to make a tradeoff between precision and recall. At last, a series of experiments are conducted on both synthetic and real-world social networks to demonstrate the effectiveness of the CPCC.

Abstract The process of matching and integrating records that relate to the same entity from one or more datasets is known as record linkage, and it has become an increasingly important subject in many application areas, including business, government and health system. The data from these areas often contain sensitive information. To prevent privacy breaches, ideally records should be linked in a private way such that no information other than the matching result is leaked in the process, and this technique is called privacy-preserving record linkage (PPRL). With the increasing data, scalability becomes the main challenge of PPRL, and many private blocking techniques have been developed for PPRL. They are aimed at reducing the number of record pairs to be compared in the matching process by removing obvious non-matching pairs without compromising privacy. However, most of them are designed for two databases and they vary widely in their ability to balance competing goals of accuracy, efficiency and security. In this paper, we propose a novel private blocking approach for PPRL based on dynamic k-anonymous blocking and Paillier cryptosystem which can be applied on two or multiple databases. In dynamic k-anonymous blocking, our approach dynamically generates blocks satisfying k-anonymity and more accurate values to represent the blocks with varying k. We also propose a novel similarity measure method which performs on the numerical attributes and combines with Paillier cryptosystem to measure the similarity of two or more blocks in security, which provides strong privacy guarantees that none information reveals even collusion. Experiments conducted on a public dataset of voter registration records validate that our approach is scalable to large databases and keeps a high quality of blocking. We compare our method with other techniques and demonstrate the increases in security and accuracy.