Information Processing & Management最新文献

{"title":"Membership inference attacks via spatial projection-based relative information loss in MLaaS","authors":"Zehua Ding ,&nbsp;Youliang Tian ,&nbsp;Guorong Wang ,&nbsp;Jinbo Xiong ,&nbsp;Jinchuan Tang ,&nbsp;Jianfeng Ma","doi":"10.1016/j.ipm.2024.103947","DOIUrl":"10.1016/j.ipm.2024.103947","url":null,"abstract":"<div><div>Machine Learning as a Service (MLaaS) has significantly advanced data-driven decision-making and the development of intelligent applications. However, the privacy risks posed by membership inference attacks (MIAs) remain a critical concern. MIAs are primarily classified into score-based and perturbation-based attacks. The former relies on shadow data and models, which are difficult to obtain in practical applications, while the latter depends solely on perturbation distance, resulting in insufficient identification performance. To this end, we propose a Spatial Projection-based Relative Information Loss (SPRIL) MIA to ascertain the sample membership by flexibly controlling the size of perturbations in the noise space and integrating relative information loss. Firstly, we analyze the alterations in predicted probability distributions induced by adversarial perturbations and leverage these changes as pivotal features for membership identification. Secondly, we introduce a spatial projection technique that flexibly modulates the perturbation amplitude to accentuate the difference in probability distributions between member and non-member data. Thirdly, this quantifies the distribution difference by calculating relative information loss based on KL divergence to identify membership. SPRIL provides a solid method to assess the potential risks of DNN models in MLaaS and demonstrates its efficacy and precision in black-box and white-box settings. Finally, experimental results demonstrate the effectiveness of SPRIL across various datasets and model architectures. Notably, on the CIFAR-100 dataset, SPRIL achieves the highest attack accuracy and AUC, reaching 99.27% and 99.73%, respectively.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103947"},"PeriodicalIF":7.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Higher-order structure based node importance evaluation in directed networks","authors":"Meng Li ,&nbsp;Zhigang Wang ,&nbsp;An Zeng ,&nbsp;Zengru Di","doi":"10.1016/j.ipm.2024.103948","DOIUrl":"10.1016/j.ipm.2024.103948","url":null,"abstract":"<div><div>Evaluating the significance of objects with possible relevant information is a crucial topic in information science. Due to the fact that objects related to each other can often be described using complex networks, this topic also forms a fundamental theme in network science. Most traditional methods for characterizing the importance of nodes in complex networks only utilize the binary relationships between node pairs, neglecting the influence brought by higher-order structures. Considering the specific interaction modes between local nodes in the network, this paper associates the higher-order structural characteristics of the network with the importance of the nodes. It constructs an evaluation framework for the importance of nodes in directed networks based on higher-order structures. Experimental analysis on both artificial data and scientific citation data from the APS dataset has validated the effectiveness of the proposed algorithms. Compared with PageRank and eigenvector centrality, the proposed algorithms demonstrated higher accuracy, revealing the role of higher-order structures in node importance evaluation. Finally, a robustness analysis of several algorithms indicated that the proposed algorithms exhibited good robustness.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103948"},"PeriodicalIF":7.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-view graph contrastive representation learning for bundle recommendation","authors":"Peng Zhang ,&nbsp;Zhendong Niu ,&nbsp;Ru Ma ,&nbsp;Fuzhi Zhang","doi":"10.1016/j.ipm.2024.103956","DOIUrl":"10.1016/j.ipm.2024.103956","url":null,"abstract":"<div><div>Bundle recommendation can recommend a collection of associated items that can be consumed together to a user rather than recommending these items separately, making it extremely suitable for some scenarios such as product bundle recommendation and game bundle recommendation. Recent bundle recommendation approaches consider auxiliary data to mitigate sparse user-bundle interactions. However, these approaches obtain the node embeddings directly from the established user-bundle graph and do not explicitly exploit the relationships between users (bundles) when constructing recommendation models. Moreover, bundle recommendation approaches based on graph contrastive learning usually construct contrastive views by randomly discarding nodes (edges) in the graph, while discarding some essential nodes or edges will destroy the structure of the original graph, thereby deteriorating the quality of the learned node embeddings. Aiming at these limitations, we propose a bundle recommendation approach based on multi-view graph contrastive representation learning. First, we present a multi-view modeling method to model the relations between entities as several views from different perspectives. These views serve as inputs of graph neural networks for graph representation learning and provide contrastive views for the contrastive learning tasks. Second, we propose a novel framework for bundle recommendation. This framework obtains the user (bundle) embeddings from different views by performing multi-view graph representation learning and enhances the learned user and bundle embeddings through a two-level contrastive learning strategy. On this basis, the enhanced user (bundle) embeddings are fused for prediction. Finally, we design a joint optimization objective to optimize the model parameters, combining the prediction loss that supports multiple negative samples and the contrastive losses. Experiments on the Netease and Youshu datasets reveal that our approach outperforms the state-of-the-art (SOTA) baselines. Furthermore, the average improvements of Recall@K and NDCG@K of our approach over the SOTA baselines are approximately 3.38% and 2.80% on Netease and 3.94% and 4.84% on Youshu.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103956"},"PeriodicalIF":7.4,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the dynamics of group-based internet rumors propagation: A novel model from the perspective of random hypergraphs","authors":"Yang Xia ,&nbsp;Haijun Jiang ,&nbsp;Shuzhen Yu","doi":"10.1016/j.ipm.2024.103941","DOIUrl":"10.1016/j.ipm.2024.103941","url":null,"abstract":"<div><div>Group interactions have become an important way of online communication today. In this paper, a novel random Hyper-ISDR rumor model is proposed, which uses random hypergraphs to describe the group relationship more accurately. A key innovation of our model is the introduction of hyperpath and path indicators into the group propagation characterization for the first time, explaining the multiple path selectivity present in group propagation. Then, the theoretical conditions for the disappearance and persistence of Internet rumors are obtained by applying stochastic stability theory. This paper finds three interesting results: (1) the propagation threshold on hypergraphs is more sensitive to parameter changes than on traditional graphs; (2) the multiple selectivity of the group propagation path is a critical catalyst for swift rumor diffusion; (3) educating spreaders to become refuters rather than removers is more effective in controlling rumors. Moreover, compared with the graph-based ISDR model and the Hyper-SIR model, it shows that the hyperdegree and path indicators have a greater impact on rumor volatility. Finally, the reliability and applicability of the results are verified by numerical simulation and a real-life case study. This work not only opens up a new perspective of group rumor dynamics analysis, but also provides a superior framework for understanding and managing online information diffusion.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103941"},"PeriodicalIF":7.4,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimal multi-scale and multi-factor two-stage integration paradigm coupled with investor sentiment for carbon price prediction","authors":"Jujie Wang,&nbsp;Xuecheng He","doi":"10.1016/j.ipm.2024.103953","DOIUrl":"10.1016/j.ipm.2024.103953","url":null,"abstract":"<div><div>The accurate prediction of carbon emission trading prices is of great significance for the effective allocation of carbon resources, achieving energy conservation, emission reduction, and green development. However, it is difficult to fully extract the fluctuation information of carbon price, and external factors also have complex impacts on it, so it is a challenge to accurately predict carbon price. Therefore, this study proposes an optimal multi-scale and multi-factor two-stage integration paradigm coupled with investor sentiment for carbon price prediction. Firstly, an adaptive periodic variational mode decomposition (APVMD) method is proposed to capture feature subsequences with different fluctuation information from a periodic perspective in carbon prices. Then a comprehensive impact factor library is constructed to assist in prediction, including unstructured data on investor sentiment and structured data. Through the enhanced light gradient boosting machine (ELightGBM) algorithm, the optimal driving factors for each feature subsequence are fully screened, and the dimensionality of the data is reduced based on their nonlinear relationship. Considering the selection of hyperparameters and the contribution of different feature subsequences, an optimized two-stage integrated prediction is designed to achieve high-precision point prediction. On this basis, uncertainty analysis is used to obtain reasonable interval prediction results. Through comparative analysis, this model is better than other comparative models in terms of predictive ability and stability.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103953"},"PeriodicalIF":7.4,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sequential recommendation by reprogramming pretrained transformer","authors":"Min Tang ,&nbsp;Shujie Cui ,&nbsp;Zhe Jin ,&nbsp;Shiuan-ni Liang ,&nbsp;Chenliang Li ,&nbsp;Lixin Zou","doi":"10.1016/j.ipm.2024.103938","DOIUrl":"10.1016/j.ipm.2024.103938","url":null,"abstract":"<div><div>Inspired by the success of Pre-trained language models (PLMs), numerous sequential recommenders attempted to replicate its achievements by employing PLMs’ efficient architectures for building large models and using self-supervised learning for broadening training data. Despite their success, there is curiosity about developing a large-scale sequential recommender system since existing methods either build models within a single dataset or utilize text as an intermediary for alignment across different datasets. However, due to the sparsity of user–item interactions, unalignment between different datasets, and lack of global information in the sequential recommendation, directly pre-training a large foundation model may not be feasible.</div><div>Towards this end, we propose the <span>RecPPT</span> that firstly employs the GPT-2 to model historical sequence by training the input item embedding and the output layer from scratch, which avoids training a large model on the sparse user–item interactions. Additionally, to alleviate the burden of unalignment, the <span>RecPPT</span> is equipped with a reprogramming module to reprogram the target embedding to existing well-trained proto-embeddings. Furthermore, <span>RecPPT</span> integrates global information into sequences by initializing the item embedding using an SVD-based initializer. Extensive experiments over four datasets demonstrated the <span>RecPPT</span> achieved an average improvement of 6.5% on NDCG@5, 6.2% on NDCG@10, 6.1% on Recall@5, and 5.4% on Recall@10 compared to the baselines. Particularly in few-shot scenarios, the significant improvements in NDCG@10 confirm the superiority of the proposed method.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103938"},"PeriodicalIF":7.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moment matching of joint distributions for unsupervised domain adaptation","authors":"Bo Zhang ,&nbsp;Xiaoming Zhang ,&nbsp;Zhibo Zhou ,&nbsp;Yun Liu ,&nbsp;Yancong Li ,&nbsp;Feiran Huang","doi":"10.1016/j.ipm.2024.103944","DOIUrl":"10.1016/j.ipm.2024.103944","url":null,"abstract":"<div><div>Unsupervised Domain Adaptation (UDA) is designed to transfer acquired knowledge from the source domain to an unlabeled target domain. In this paper, we present a comprehensive approach that seamlessly addresses both source-available and source-free UDA by matching the joint distributions across domains, independent of the availability of source data. Our methodology introduces three innovative criteria to quantitatively assess the divergences between the source and target data, as well as between the source model hypothesis and target data. The criteria decide whether the predicted labels of the target hypothesis are affected by the other knowledge of both domains in the form of a precise formula, thereby enabling targeted supervision in UDA. We evaluate the effectiveness through 37 image and text classification tasks across four different datasets, comparing their performance against the state-of-the-art models. Experiments demonstrate that the proposed approaches obtain superior accuracies for most of the tasks, especially for the source-free setting, which still exceeds HOMDA 0.6% on Office and DRDA 1.5% on Office-Home, even without direct access to source data.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103944"},"PeriodicalIF":7.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantity forecast of mobile subscribers with Time-Dilated Attention","authors":"Binhong Yao","doi":"10.1016/j.ipm.2024.103940","DOIUrl":"10.1016/j.ipm.2024.103940","url":null,"abstract":"<div><div>The quantity forecast of mobile subscribers requires accurate and reliable results for obtaining insights into user trends and facilitating effective business management. Due to the complexity inherent in mobile subscriber data, influenced by subscriber tendencies and device popularity, capturing its underlying regularities poses a challenge. In this research, a novel Time-Dilated Attention (TDA) model is proposed, complemented by a feature extraction method characterized by high interpretability and distinguishability. Its efficacy and implications are explored on a real-world mobile subscriber dataset. TDA facilitates the acquisition of more informative representations, while our feature extraction method enhances the ability to discern dissimilar samples, thereby improving the stability of mobile subscriber trend analysis. The approach is validated on three additional datasets to assess its robustness. Experimental findings on the target mobile subscriber dataset demonstrate that the proposed approach achieves reductions in MAE, RMSE, and Theil’s U by 1.45%, 5.28%, and 5.12%, respectively, compared to the strongest baseline methods. Additionally, it attains the second-best performance in terms of MedAE. Furthermore, this model consistently ranks within the top two positions for nine out of twelve metrics on the additional datasets, underscoring its generalizability.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103940"},"PeriodicalIF":7.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial and temporal twin-guided pattern recurrent graph network for implementing reasoning of spatiotemporal knowledge graph","authors":"Xiaobei Xu ,&nbsp;Ruizhe Ma ,&nbsp;Beijing Zhou ,&nbsp;Li Yan ,&nbsp;Zongmin Ma","doi":"10.1016/j.ipm.2024.103942","DOIUrl":"10.1016/j.ipm.2024.103942","url":null,"abstract":"<div><div>The extrapolation of knowledge graphs (KGs) has been the subject of numerous studies. However, real world data often has complex spatial attributes, which makes reasoning on spatiotemporal knowledge graphs (STKGs) challenging. In response, we propose a model that captures both temporal and spatial patterns to address the challenge of predicting future facts in STKGs. The proposed spatial and temporal twin-guided pattern recurrent graph network (STTP-RGN) utilizes temporal and spatial sequences to identify cyclic and repetitive patterns in data. It performs spatiotemporal-twin encoding and temporal and spatial sequence encoding respectively, and inputs the encoded three results into three corresponding decoders to determine the evolution of entity and predicate representations in time and space. We used the YAGO10K, Wikidata40K, Opensky18K and DY-NB21K for tests on entity and predicate prediction. On YAGO10K, the model's entity prediction performance outperforms the best temporal extrapolation model RETIA by 20 %. The predicate and entity predictions on Wikidata40K have improved by 3 % and 20 %, respectively. Results for entity prediction on Opensky18K have increased by 30 %, while results for predicate prediction have improved by 1 %. The experimental results demonstrate that the model fills the gap in knowledge extrapolation on STKG.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103942"},"PeriodicalIF":7.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive survey on social engineering attacks, countermeasures, case study, and research challenges","authors":"Tejal Rathod ,&nbsp;Nilesh Kumar Jadav ,&nbsp;Sudeep Tanwar ,&nbsp;Abdulatif Alabdulatif ,&nbsp;Deepak Garg ,&nbsp;Anupam Singh","doi":"10.1016/j.ipm.2024.103928","DOIUrl":"10.1016/j.ipm.2024.103928","url":null,"abstract":"<div><div>Social engineering attacks are inevitable and imperil the integrity, security, and confidentiality of the information used on social media platforms. Prominent technologies, such as blockchain, artificial intelligence (AI), and proactive access controls, were adopted in the literature to confront the social engineering attacks on social media. Nevertheless, a comprehensive survey on this topic is notably absent from the current body of research. Inspired by that, we propose an exhaustive survey comprising an in-depth analysis of 10 distinct social engineering attacks with their real-time scenarios. Furthermore, a detailed solution taxonomy is presented, offering valuable insights (e.g., objective, methodology, and results) to tackle social engineering attacks effectively. Based on the solution taxonomy, we propose an AI and blockchain-based malicious uniform resource locator (URL) detection framework (as a case study) to confront social engineering attacks on the Meta platform. For that, a standard dataset is utilized, which comprises 12 different datasets containing 3980870 malicious and non-malicious URLs. To classify URLs, a binary classification problem is formulated and solved by using different AI classifiers, such as Naive Bayes (NB), decision tree (DT), support vector machine (SVM), and boosted tree (BT). The non-malicious URLs are forwarded to the blockchain network to ensure secure storage, strengthening the effectiveness of the malicious URL detection framework. The proposed framework is evaluated with baseline approaches, wherein the NB achieves noteworthy training accuracy, i.e., 76.87% and training time of (8.23 (s)). Additionally, interplanetary file system (IPFS)-based blockchain achieves a remarkable response time, i.e., (0.245 (ms)) compared to the conventional blockchain technology. We also used execution cost and smart contract vulnerability assessment using Slither to showcase the outperformance of blockchain technology. Lastly, we shed light on the open issues and research challenges of social engineering attacks where research gaps still exist and require further investigation.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103928"},"PeriodicalIF":7.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}