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HilAnchor:Location Privacy Protection in the Presence of Users' Preferences

Journal of computer Science & Technology 2012年第2期27卷 413-427页

作者：倪巍伟郑锦旺崇志宏 School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education

Location privacy receives considerable attentions in emerging location based *** current practices however either ignore users＇ preferences or incompletely fulfill privacy *** this paper,we propose a privacy protection solution to allow users＇ preferences in the fundamental query of k nearest neighbors （kNN）.Particularly,users are permitted to choose privacy preferences by specifying minimum inferred *** Hilbert curve based transformation,the additional workload from users＇ preferences is ***,this transformation reduces time-expensive region queries in 2-D space to range the ones in 1-D ***,the time efficiency,as well as communication efficiency,is greatly improved due to clustering properties of Hilbert ***,details of choosing anchor points are theoretically *** empirical studies demonstrate that our implementation delivers both flexibility for users＇ preferences and scalability for time and communication costs.

关键词： location privacy kNN query minimum inferred region users＇ privacy preferences

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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FLSSM: A Federated Learning Storage Security Model with Homomorphic Encryption

arXiv

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arXiv 2025年

作者： Li, Yang Xia, Chunhe Li, Chang Li, Xiaojian Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing100191 China Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China Guangxi Collaborative Innovation Center Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China College of Computer Science and Information Technology Guangxi Normal University Guilin541004 China School of Cyber Science and Technology Beihang University Beijing100191 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

Federated learning based on homomorphic encryption has received widespread attention due to its high security and enhanced protection of user data privacy. However, the characteristics of encrypted computation lead to three challenging problems: "computation-efficiency", "attack-tracing" and "contribution-assessment". The first refers to the efficiency of encrypted computation during model aggregation, the second refers to tracing malicious attacks in an encrypted state, and the third refers to the fairness of contribution assessment for local models after encryption. This paper proposes a federated learning storage security model with homomorphic encryption (FLSSM) to protect federated learning model privacy and address the three issues mentioned above. First, we utilize different nodes to aggregate local models in parallel, thereby improving encrypted models’ aggregation efficiency. Second, we introduce trusted supervise nodes to examine local models when the global model is attacked, enabling the tracing of malicious attacks under homomorphic encryption. Finally, we fairly reward local training nodes with encrypted local models based on trusted training time. Experiments on multiple real-world datasets show that our model significantly outperforms baseline models in terms of both efficiency and security metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Identifying Abnormal Hosts in Data Streams Using Reversible Sketch

Engineering Reports

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Engineering Reports 2025年第5期7卷

作者： Aiping Zhou Jin Qian School of Information Engineering Taizhou University Taizhou China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing China

Significant cardinality change is an important sign of the beginning of network attacks. Hosts associated with significant cardinality changes usually exhibit abnormal behavior. Identifying abnormal hosts is meaningful for many applications such as anomaly detection. High-speed data streams remain a great challenge to accurately estimate cardinality changes and detect abnormal hosts in real-time. Sketches are a type of probability data structure, which are widely used to compress high-rate data streams and estimate their statistics. However, most existing studies cannot simultaneously measure two kinds of cardinality changes in a distributed manner and efficiently reconstruct addresses of abnormal hosts in a centralized manner because of high calculation and memory overhead. In this paper, we propose reversible sketch-based abnormal host identification. It constructs a reversible data structure and estimates cardinality changes using a probabilistic counting approach, so that abnormal sources and destinations are simultaneously identified based on their cardinality changes between consecutive measurement periods. Moreover, addresses of abnormal hosts can be reconstructed by only simple inverse calculation to find out attackers and victims. The experimental results illustrate that the proposed approach obtains superior performance for cardinality change estimation and addresses of abnormal host reconstruction in accuracy and performance compared with the existing approaches.

关键词： anomaly detection cardinality change estimation data streams reversible sketch

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A Direction-Based Data Forwarding Algorithm for Opportunistic networks

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Journal of Electronic Science and Technology 2016年第2期14卷 152-159页

作者： Jie Yu Yue Ling Si-Ying Li Hui-Qi Fang Lin-Feng Liu School of Computer Science and Technology Nangjing University of Posts and Telecommunications Laboratory of Computer Network and Information Integration Southeast University

In this paper, the forwarding objective and mobility law of nodes in opportunistic networks are first investigated to establish a mathematical model for further analysis, then a gradually accelerated data forwarding algorithm is proposed. In this algorithm, according to the distance between data carriers （nodes） and the destination, some intermediate nodes are selected to relay the data. Especially, the forwarded copies can be increased when the delay reaches a threshold, to guarantee the required delivery ratio. The simulation results show that the proposed algorithm can effectively reduce the storage occupancies of nodes and forwarding delay, and guarantee the delivery ratio simultaneously.

关键词： Accelerated delivery ratio,forwarding opportunistic networks storageoccupancies.

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Asynchronous Tag-based Physical-Layer Authentication in Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Tan, Haijun Xu, Yuntao Du, Jiewei Xie, Ning Li, Hongbin Peng Cheng Laboratory Control and Network Foundation Laboratory Frontier Research Center Shenzhen518107 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China National Innovation Center of High Speed Train Qingdao266111 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Tag-based physical-layer authentication (PLA) has gained significant research interest due to its high security and low complexity compared to traditional upper-layer authentication mechanisms. However, conventional tag-based PLA schemes often require strict synchronization between the tag and the source message before transmission, which may limit their practicality and effectiveness. To address this limitation, we propose an asynchronous tag-based PLA scheme, where the tag and the source message are transmitted asynchronously with an intentional time delay introduced at the transmitter. The proposed scheme enhances both compatibility and security without compromising robustness. We provide a theoretical analysis of the proposed scheme, and derive closed-form expressions to characterize its performance. Furthermore, theoretical comparisons with existing methods yield insightful conclusions regarding the advantages of the proposed approach. The scheme is implemented and extensively evaluated through simulations, where the theoretical results exhibit strong agreement with the simulation outcomes, thereby validating its effectiveness. © 2025 IEEE.

关键词： asynchronous closed form intentional timing delay Physical-layer authentication tag-based

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Design and Implementation of Not So Cooperative Caching System

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China Communications 2015年第7期12卷 31-46页

作者： HU Xiaoyan GONG Jian CHENG Guang ZHANG Weiwei Ahmad Jakalan School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

Not So Cooperative Caching(NSCC) considers a network comprised of selfish nodes; each is with caching capability and an objective of reducing its own access cost by fetching data from its local cache or from neighboring caches. These nodes would cooperate in caching and share cached content if and only if they each benefit. The challenges are to determine what objects to cache at each node and to implement the system in the context of information Centric networking(ICN). This work includes both a solution for the NSCC problem and a design and implementation of an NSCC system in Named Data networking(NDN), a large effort that exemplifies ICN. Our design applies NDN synchronization protocol to facilitate the information exchange among nodes, adopts group key encryption to control data access within the NSCC group, and offers an error checker to detect error events in the system. Our approach is validated by deploying the system we developed on Planet Lab.

关键词： named data networking content caching selfish caches synchronization group rekeying error checker game theory

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Parallel Sketch Based Super Node Detection with Traceability

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Chinese Journal of Electronics 2018年第6期27卷 1133-1140页

作者： ZHOU Aiping LIU Lijun ZHU Huisheng ZHU Chen'gang CHEN Lin School of Computer Science and Technology Taizhou UniversityTaizhou 225300 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

Traffic measurement and monitoring is crucial for network applications, such as network security, network management and so on. One central problem is to detect super nodes, which have significant change of connection degree between consecutive measurement *** to weakness in massive network traffic processing for the centralized algorithm and low detection accuracy, space efficiency for super node detection algorithm based on flow sampling, we propose Parallel sketch based super node detection with traceability(PSD). It constructs parallel sketch and estimates connection degree of nodes by probabilistic counting approach, so that super nodes are identified using connection degree change between consecutive measurement periods. Moreover, IP addresses of super nodes are reconstructed by simple computing to trace attacker or victims. The experimental results illustrate that the proposed method outperforms the Compact spread estimator(CSE) and Data streaming and sampling(DSS) in terms of detection accuracy and storage utilization.

关键词： network measurement Super node detection Parallel sketch network data stream

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A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering

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Big Data Mining and Analytics 2018年第3期1卷 211-221页

作者： Yu Liu Shuai Wang M.Shahrukh Khan Jieyu He School of Computer Science and Engineering and also with MOE Key Laboratory of Computer Network and Information Integration Southeast University

Due to the widespread availability of implicit feedback(e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback,implicit feedback cannot directly reflect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems,some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. However, there is as yet no research combining collaborative filtering and contentbased recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders(DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods.

关键词： hybrid recommender system neural collaborative filtering auto-encoder implicit feedback

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YOLO-CORE: Contour Regression for Efficient Instance Segmentation

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Machine Intelligence Research 2023年第5期20卷 716-728页

作者： Haoliang Liu Wei Xiong Yu Zhang School of Computer Science and Engineering and the Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast UniversityNanjing 211189China

Instance segmentation has drawn mounting attention due to its significant ***,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level *** this paper,we present a conceptually efficient contour regression network based on the you only look once(YOLO)architecture named YOLO-CORE for instance *** mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multiorder constraint consisting of a polar distance loss and a sector *** proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and *** achieves 57.9%AP@0.5 with 47 FPS(frames per second)on the semantic boundaries dataset(SBD)and 51.1%AP@0.5 with 46 FPS on the COCO *** superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding ***,our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost(65.86 BFLOPs(billion float operations per second)to 66.15 BFLOPs with the YOLOv3 detector).

关键词： computer vision instance segmentation object shape prediction contour regression polar distance.

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