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Efficient and Privacy-Preserving Weighted Nearby-Fit Spatial keyword Query in Cloud

IEEE Internet of Things Journal 2025年

作者： Sun, Lili Lu, Rongxing Zheng, Yandong Zhang, Yonggang Tao, Yi Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Jilin University College of Software Changchun130012 China

In the modern digital landscape, integrating geographic locations and textual descriptions within a geo-textual dataset enhances location-based services (LBS) via spatial keyword queries, as these queries combine spatial and textual information to deliver more precise and personalized results. Additionally, the advent of cloud computing allows data owners to outsource data management and services to the cloud, boosting scalability but introducing efficiency challenges due to complex encryption. Although many schemes have been proposed for spatial keyword queries on encrypted geo-textual data, none supports matching a query keyword set with the keyword sets of multiple objects, a common query type in LBS. Imagine a user seeking to rent a house close to his/her workplace, with easy access to conveniences like supermarkets. By using nearby-fit spatial keyword queries, we can match the desired house with a house-type target object and its nearby amenities, offering more practical and flexible recommendations than traditional spatial keyword queries. Hence, in this paper, we introduce an efficient and privacy-preserving scheme called the Privacy-preserving Weighted Nearby-fit Spatial keyword (PWNSK) query scheme. First, we design a target-oriented spatial keyword (TOSK) tree for data organization and a TOSK tree based WNSK query algorithm for efficient pruning by simultaneously utilizing locations, keywords, and distances from nearby objects to target objects. For privacy, we develop several protocols, including one for polynomial coefficient re-encoding, based on polynomial coefficient encoding and fully homomorphic encryption. Building on these protocols, we introduce our PWNSK scheme. A thorough security analysis confirms its robustness, while extensive experiments also showcase its effectiveness. © 2014 IEEE.

关键词： Differential privacy

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Big data challenge： a data management perspective

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Frontiers of Computer Science 2013年第2期7卷 157-164页

作者： Jinchuan CHEN Yueguo CHEN Xiaoyong DU Cuiping LI Jiaheng LU Suyun ZHAO Xuan ZHOU Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China China Beijing 100872

There is a trend that, virtually everyone, rang- ing from big Web companies to traditional enterprisers to physical science researchers to social scientists, is either al- ready experiencing or anticipating unprecedented growth in the amount of data available in their world, as well as new op- portunities and great untapped value. This paper reviews big data challenges from a data management respective. In partic- ular, we discuss big data diversity, big data reduction, big data integration and cleaning, big data indexing and query, and fi- nally big data analysis and mining. Our survey gives a brief overview about big-data-oriented research and problems.

关键词： big data performance databases

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Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Liu, Bei Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Kim, Dong In The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective Computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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A Hierarchical Encrypted Compression Scheme for Intra-Vehicle Network

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Shang, Chao Cao, Jin Li, Zejian Niu, Ben Lam, Kwok-Yan Chi, Chi-Hung Li, Hui Xidian University State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xi'an710126 China Nanyang Technological University College of Computing and Data Science National Digital Trust Centre Singapore639798 Singapore Chinese Academy of Sciences State Key Laboratory of Information Security Institute of Information Engineering Beijing100094 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Nanyang Technological University National Digital Trust Centre Singapore639798 Singapore

The CAN bus is the most widely used bus for intra-vehicle communication due to its high transmission stability, excellent real-time communication capability, and relatively low cost. As the number of ECUs grows, the CAN bus load increases and thus raises the possibility of data transmission delays and errors. Message compression based on the differential algorithm has been proposed to reduce the CAN bus load. However, current works do not consider the security problems of the CAN bus. Attackers can manage to acquire the original messages before compression and disturb the message statistics to decrease compression rate by injecting malicious frames. In this paper, we propose a secure compression mechanism for the intra-vehicle network, including an improved compression algorithm, a stream key distribution scheme, and a hierarchical encryption scheme. Formal verification results show that the proposed scheme can achieve mutual authentication, message confidentiality and integrity, resist replay attacks, and support secure compression. Evaluations using real vehicle data on 16 MHz boards show the average communication overhead can be reduced by 46.38% compared to the original messages. Performance analysis results show our scheme can reduce computational overhead on compression by 31.82% and 19.43% on decompression compared to related schemes. © 2000-2011 IEEE.

关键词： Buses

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Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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MRST-- An Efficient Monitoring Technology of Summarization on Stream data

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Journal of Computer Science & Technology 2007年第2期22卷 190-196页

作者：樊小泊解婷婷李翠平陈红 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to process infinite data stream is an open challenging problem. In this paper, we adopt the wavelet and sliding window methods to design a multi-resolution summarization data structure, the Multi-Resolution Summarization Tree （MRST） which can be updated incrementally with the incoming data and can support point queries, range queries, multi-point queries and keep the precision of queries. We use both synthetic data and real-world data to evaluate our algorithm. The results of experiment indicate that the efficiency of query and the adaptability of MRST have exceeded the current algorithm, at the same time the realization of it is simpler than others.

关键词： Haar wavelet sliding window stream data

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SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking 25

SoAy: A Solution-based LLM API-using Methodology for Academi...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Yuanchun Wang Jifan Yu Zijun Yao Jing Zhang Yuyang Xie Shangqing Tu Yiyang Fu Youhe Feng Jinkai Zhang Jingyao Zhang Bowen Huang Yuanyao Li Huihui Yuan Lei Hou Juanzi Li Jie Tang School of Information Renmin University of China Beijing China & Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Institute of Education Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Information Renmin University of China Beijing China & Engineering Research Center of Database and Business Intelligence MOE Beijing China School of Information Renmin University of China Beijing China Zhipu AI Beijing China

ISBN: (纸本)9798400712456

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable *** evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://***/RUCKBReasoning/SoAy.

关键词： academic information seeking

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