Multibehavior recommender systems are proficient at constructing precise representations of users and items by leveraging a variety of interaction behaviors, e.g., click, add-to-cart, and purchase. However, they are still facing the following challenges: 1) there are a large number of spurious correlations existing in auxiliary behaviors (i.e., noise unrelated to the target behavior preference); and 2) spurious correlations may be unintentionally introduced and amplified during the representation learning process in traditional multibehavior recommenders. Hence, we propose a novel framework-disentangling spurious correlations (DISC) to measure and disentangle the spurious correlations of the multibehavior recommendation. In particular, to precisely identify the intent representations for each user, we devise a cross-behavior self-attention layer incorporating lightweight graph convolution networks to encode graph nodes under specific behaviors, thereby enabling the model to supervise the subsequent spurious correlation disentanglement. Subsequently, to disentangle the spurious correlations among multiple behaviors, we design a time-sensitive Jaccard coefficient to dynamically measure the users’ spurious correlations. Building on this, we propose a dual mutual information (MI) bound structure to disentangle spurious correlations existing in auxiliary behaviors and transfer genuine correlated semantic information to the target behavior, thus alleviating the data sparsity. Extensive experiments on three real-world datasets demonstrate the consistent improvements obtained by DISC over 11 state-of-the-art baselines by effectively disentangling the spurious correlations.

Graph Convolutional Networks (GCNs) have become increasingly popular in recommendation systems. However, recent studies have shown that GCN-based models will cause sensitive information to disseminate widely in the graph structure, amplifying data bias and raising fairness concerns. While various fairness methods have been proposed, most of them neglect the impact of biased data on representation learning, which results in limited fairness improvement. Moreover, some studies have focused on constructing fair and balanced data distributions through data augmentation, but these methods significantly reduce utility due to disruption of user preferences. In this paper, we aim to design a fair recommendation method from the perspective of data augmentation to improve fairness while preserving recommendation utility. To achieve fairness-aware data augmentation with minimal disruption to user preferences, we propose two prior hypotheses. The first hypothesis identifies sensitive interactions by comparing outcomes of performance-oriented and fairness-aware recommendations, while the second one focuses on detecting sensitive features by analyzing feature similarities between biased and debiased representations. Then, we propose a dual data augmentation framework for fair recommendation, which includes two data augmentation strategies to generate fair augmented graphs and feature representations. Furthermore, we introduce a debiasing learning method that minimizes the dependence between the learned representations and sensitive information to eliminate bias. Extensive experiments on two real-world datasets demonstrate the superiority of our proposed framework.

In this paper, we aim to tackle the challenging Black-Box Open-Set Domain Adaptation (BB-OSDA) task. BB-OSDA enables conducting Open-Set Domain Adaptation (OSDA) with solely a black-box source model, broadening the application scope of OSDA. Inspired by the significant success of pre-trained large vision-language (ViL) models in various applications, we propose a novel method, termed Knowledge Aggregation for Black-box Open-set domain adaptatioN (KABON), which leverages the power of ViL models to solve the BB-OSDA problem. Specifically, we first devise a novel knowledge aggregation approach to harness both the generic knowledge from the ViL model and the task-specific knowledge from the black-box source model. Subsequently, we utilize a Gaussian Mixture Model (GMM) with entropy criterion to divide samples of target domain into shared or novel classes. Furthermore, a self-correction strategy is proposed to refine the division of shared and novel classes. Finally, we leverage the divided samples through entropy minmax learning to simultaneously achieve shared classes adaptation and novel classes detection. Experiments conducted on multiple benchmark datasets demonstrate the effectiveness of our proposed method.

Many privacy-preserving multidimensional data aggregation (PPMDA) schemes have been proposed to safeguard user privacy and provide aggregated real-time data for the control center (CC) to optimize power allocation in the smart grid. However, without Trusted Authority (TA), existing PPMDA schemes cannot simultaneously provide lightweight encryption, achieve fault tolerance, and resist collusion attacks between fog nodes and CC. To address these issues, we propose a fault-tolerant and privacy-enhanced multidimensional data aggregation scheme without TA (FPMDA) based on fog computing. Specifically, the Chinese Remainder Theorem is leveraged to enhance the efficiency of multidimensional data processing, and an innovative dual-masking approach is introduced to ensure the security of data aggregation. In addition, employing the homomorphic property of the (t, k)-threshold secret sharing algorithm, we design a data aggregation method that enhances security and fault tolerance, making it resilient against insider attacks. Finally, compared with existing schemes, FPMDA not only significantly enhances privacy preservation while maintaining required security properties but also achieves low computational and communication load, demonstrating practicality for resource-constrained smart meters.

The fog computing makes the cloud-based internet of things to be more suitable for the time and location-sensitive applications. However, it is still facing challenges to balance the usability of data and privacy protection. In the past years, some excellent works have tried to address this concern using the aggregation method. However, the fact that IoT devices at the edge of the network may malfunction is not paid enough attention. In this paper, a fault-tolerant data aggregation scheme for fog computing networks is presented by employing Shamir's secret sharing and ElGamal cryptosystem. The proposed scheme ensures that even though a few IoT devices fail to work, the aggregated value can still be obtained with the number of IoT devices that reach the threshold of collaboration. In addition, security analysis and performance evaluation show the proposed scheme achieves security, privacy, and efficiency.

Despite the surges of deep learning-based method in dynamic scene deblurring achieves good performance, the challenges still remain a lot: (a) the running speed is far from the requirement of processing; (b) there will be inevitable information loss along with the deepening of network layers, which will further lead to the deterioration of the quality of the restored pictures. To deal with these challenges, we propose a novel learning-based model. In our method, we integrate two mechanisms for the generator based on the Generative Adversarial Nets (GAN). First, we develop the Enhanced Feature Fusion (EFF) mechanism which aims at providing multi-layer feature information to assist the image restoration. We further design Feature Multi-distillation (FMD) mechanism to filter and well fuse the multi-scale feature maps. By integrating the two mechanisms, the high-level feature maps can be progressively refined and the detailed semantic information can be properly utilized. In addition, we use the double-scale discriminator architecture which could enables the network to observe the image from the perspective of local and global respectively and obtain overall information for the whole image restoration. Extensive experimental results on the GOPRO and Kohler datasets show that our method can approach comparably to the state-of-the-arts in terms of accuracy while consuming much less inference time, which demonstrates that our method acquiring a better trade off between image restoration quality and running speed.

In recent years, with the rapid development of sensing technology and the Internet of Things (IoT), sensors play increasingly important roles in traffic control, medical monitoring, industrial production and etc. They generated high volume of data in a streaming way that often need to be processed in real time. Therefore, streaming data computing technology plays an indispensable role in the real-time processing of sensor data in high throughput but low latency. However, there are two problems in deploying streaming data process ability in cloud computing data centre. Firstly, massive sensor nodes simultaneously upload data to the remote cloud computing data centre, which requires a large number of bandwidth resources supports. The existing network infrastructure cannot provide enough bandwidth at a reasonable price. Secondly, due to the geographical distribution characteristics of the cloud computing data centre, there will inevitably be large transmission delay during the process of data transmission. Such end-to-end delay is intolerable to mobile applications especially for those latency sensitive tasks. In view of the above problems, this paper proposes an autonomous driving oriented edge streaming data processing framework, which migrates the computing and storage capability from the remote cloud data centre to the edge data centre. It focuses on the change of vehicle flow in a specific geographical area, and uses the computing power sunk to edge node to process the massive streaming data generated by autonomous vehicles nearby. The proposed framework is implemented on top of Spark Streaming, which builds up a gray model based traffic flow monitor, a traffic prediction orientated prediction layer and a fuzzy control based Batch Interval dynamic adjustment layer for Spark Streaming. It could forecast the variation of sensors data arrive rate, make streaming Batch Interval adjustment in advance and implement real-time streaming process by edge. Therefore, it can realise the monitor and prediction of the data flow changes of the autonomous driving vehicle sensor data in geographical coverage of edge computing node area, meanwhile minimise the end-to-end latency but satisfy the application throughput requirements. The experiments show that it can predict short-term traffic with no more than 4% relative error in a whole day. By making batch consuming rate close to data generating rate, it can maintain system stability well even when arrival data rate changes rapidly. The Batch Interval can be converged to a suitable value in two minutes when data arrival rate is doubled. Compared with vanilla version Spark Streaming, where there has serious task accumulation and introduces large delay, it can reduce 35% latency by squeezing Batch Interval when data arrival rate is low; it also can significantly improve system throughput by only at most 25% Batch Interval increase when data arrival rate is high.

Electricity theft occurs from time to time in the smart grid, which can cause great losses to the power supplier, so it is necessary to prevent the occurrence of electricity theft. Using machine learning as an electricity theft detection tool can quickly lock participants suspected of electricity theft; however, directly publishing user data to the detector for machine learning‐based detection may expose user privacy. In this paper, we propose a real‐time fault‐tolerant and privacy‐preserving electricity theft detection (FPETD) scheme that combines n‐source anonymity and a convolutional neural network (CNN). In our scheme, we designed a fault‐tolerant raw data collection protocol to collect electricity data and cut off the correspondence between users and their data, thereby ensuring the fault tolerance and data privacy during the electricity theft detection process. Experiments have proven that our dimensionality reduction method makes our model have an accuracy rate of 92.86% for detecting electricity theft, which is much better than others.

In smart grids (SG), data aggregation is widely used to strike a balance between data usability and privacy protection. The fault tolerance is an important requirement to improve the robustness of data aggregation protocols, which enables normal execution of the protocols even with failures on some entities. However, to achieve fault tolerance, most schemes either sacrifice the aggregation accuracy due to the use of differential privacy or substitution strategy or need to rely on an online trusted entity to manage all user blinding factors. In this paper, a ( k,n ) threshold privacy-preserving data aggregation scheme named ( k,n )-PDA is proposed, which reconciles data usability and data privacy through the BGN cryptosystem and achieves fault tolerance with accurate aggregation using Shamir’s secret sharing without any online trusted entity. Besides, our scheme supports the efficient changing of users’ membership. Specifically, the dynamic secrete key is distributed to n smart meters (SMs) through the threshold secret sharing algorithm. When k or more meters participate in the aggregation, the data service center (DSC) can reconstruct the key to compute the aggregate results, and less than k SMs cannot recover the key. Thus, our solution still works functionally even if up to n−k SMs fail; also, it resists attacks from the collusion of less than k SMs. Moreover, system and performance analyses demonstrate that our scheme achieves privacy, fault tolerance, and membership dynamics with high efficiency.