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Scalable Fuzzy keyword Ranked Search Over Encrypted Data on Hybrid Clouds

IEEE TRANSACTIONS ON CLOUD COMPUTING 2023年第1期11卷 308-323页

作者： Zhang, Hua Zhao, Shaohua Guo, Ziqing Wen, Qiaoyan Li, Wenmin Gao, Fei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Searchable encryption (SE) is a powerful technology that enables keyword-based search over encrypted data becomes possible. However, most SE schemes focus on exact keyword search which can not tolerate misspellings and typos. Existing fuzzy keyword search schemes only support fuzzy search within a limited similarity threshold d, the storage cost will grow exponentially or the precision of search results will greatly decrease as d increases. Moreover, the current fuzzy keyword ranked search schemes consider only the keyword weight, and disregard the influence of keyword morphology similarity on the ranking. In this article, we propose a scalable fuzzy keyword ranked search scheme over encrypted data under hybrid clouds architecture. We use the edit distance to measure the similarity of keywords and design an edit distance algorithm over encrypted data, in which our scheme achieves fuzzy keyword search for any similarity threshold d with a constant storage size and accurate search results. Furthermore, we design a two factor ranking function combining keyword weight with keyword morphology similarity, which is utilized to rank the search results and enhance system usability. Extensive experiments are performed to demonstrate the trade-off of efficiency and security of the proposed scheme.

关键词： Cloud computing keyword search Indexes Scalability Encryption Morphology Switches Searchable encryption fuzzy keyword search edit distance data security cloud computing

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Joint Mode Selection and Beamforming for IRS-Aided Maritime Cooperative Communication Systems

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND networking 2023年第1期7卷 57-69页

作者： Cao, Xueyan Hu, Xiaoling Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

In this paper, we consider an intelligent reflecting surface (IRS) aided maritime cooperative communication system, where multiple relay ships each equipped with an active decode-and-forward relay and a passive IRS assist the downlink transmission between the base station (BS) and the destination ship. Each relay ship operates in either the active mode with both active relaying and IRS reflection or the passive mode with only IRS reflection. We formulate and solve new problems to minimize the total transmit power of the BS and active relay ships, by jointly optimizing the relay mode selection vector, transmit power, as well as active and passive beamforming, subject to the quality of service of the destination ship. The joint optimization problem is solved by alternatively solving two sub-problems, i.e., the joint mode selection and power control sub-problem, and the joint active and passive beamforming sub-problem. Two algorithms based on exhaustive searching and many-to-one matching are respectively proposed for solving the first sub-problem, while a semidefinite programming-based algorithm is proposed for solving the second sub-problem. Numerical results demonstrate the good performance of the proposed joint mode selection and beamforming algorithm and the advantage of the proposed IRS-aided maritime cooperative communication system in power saving.

关键词： Relays Array signal processing Marine vehicles Maritime communications Power control Downlink Cooperative communication Intelligent reflecting surface maritime cooperative communications joint active and passive beamforming

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DAWN: Domain Generalization Based Network Alignment

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IEEE TRANSACTIONS ON BIG DATA 2023年第3期9卷 878-888页

作者： Gao, Shuai Zhang, Zhongbao Su, Sen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Network alignment aims to discover nodes in different networks belonging to the same identity. In recent years, the network alignment problem has aroused significant attentions in both industry and academia. With the rapid growth of information, the sizes of networks are usually very large and in most cases we only focus on the alignment of partial networks. However, under this circumstances, the collected network data may be highly biased, and the training and testing data are no longer i.i.d. (identically and independently distributed). Thus, it is difficult for the trained alignment model to have a good performance in the test set. To bridge this gap, in this paper, we propose a novel Domain generAlization based netWork aligNment approach termed as DAWN. Specifically, in DAWN, we first design a novel invariant feature extraction model which leverages adversarial learning to extract domain-invariant features. Then, we design a novel invariant network alignment model which can achieve global optimum and local optimum simultaneously to learn domain-invariant alignment patterns. Finally, we conduct extensive experiments on the benchmark dataset of Facebook-Twitter, and results show that DAWN can averagely achieve 14.01% higher Hits@k and 10.63% higher MRR@k compared with the state-of-the-art methods.

关键词： Feature extraction Training Task analysis Switches Social networking (online) Knowledge engineering Big Data Network alignment domain generalization adversarial learning deep learning

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Enhanced covertness class discriminative universal adversarial perturbations

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NEURAL NETWORKS 2023年第1期165卷 516-526页

作者： Gao, Haoran Zhang, Hua Zhang, Xin Li, Wenmin Wang, Jiahui Gao, Fei Beijing Univ Posts & Telecommun State key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The main aim of class discriminative universal adversarial perturbations (CD-UAPs) is that the adversary can flexibly control the targeted class and influence remaining classes limitedly. CD-UAPs generated by the existing attack strategies suffer from a high fooling ratio of non-targeted source classes under non-targeted and targeted attacks, and face the increasing risk of discovery. In this paper, we propose a training framework for generating enhanced covertness CD-UAPs. It trains the targeted source class set and the non-targeted source classes set alternately to update the perturbation and introduces logit pairing to mitigate the influence of perturbation on the non-targeted source classes set. Further, we extend CD-UAPs on the targeted (one-targeted) attack to the multi-targeted attack, which perturbs a targeted source class to multiple targeted sink classes that seriously threaten the current scenario. It can not only provide the adversary with freedom of precise attack but reduce the risk of being detected. This attack poses a strong threat to security-sensitive applications. Extensive experiments on the CIFAR-10, CIFAR-100 and ImageNet datasets show our method can generate more deceptive perturbations and enhance the covertness of CD-UAPs. For example, our method improves the absolute fooling ratio gaps of ResNet-20 and VGG-16 by 9.46% and 6.94% compared with the baseline method, respectively. We achieve the multi-targeted attack with a high fooling ratio on the GTSRB dataset. The average absolute target fooling ratio gaps of ResNet-20 and VGG-16 are 81.89% and 76.33%, respectively.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Adversarial attacks Universal adversarial perturbations Deep neural networks

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Barycenter Calibration With High Order Spectra of Windowed Delay-Doppler Signals for OTFS Based ISAC Systems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 2450-2466页

作者： Yang, Yiran Pan, Yuchen Liu, Xiqing Peng, Mugen Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Integrated sensing and communication (ISAC) technology stands as a pivotal enabler for the sixth-generation mobile communication systems (6G) to enable high data rate communication while ensuring accurate target sensing. Orthogonal time-frequency space (OTFS) modulation has emerged as a promising candidate for ISAC, owing to its remarkable performance in doubly-selective channels. However, the sensing resolutions of OTFS are limited by the available bandwidth and frame time. In cases of insufficient delay and Doppler resolutions, the channel response is spread along delay and Doppler axes, resulting in substantial sensing inaccuracies. In our previous research, we introduced the amplitude barycenter calibration (A-BC) algorithm as an effort to improve the accuracy of parameter estimation, albeit without a strategic design for OTFS windows. In this work, we delve into the receive window design of the OTFS-ISAC system and develop a barycenter calibration algorithm that explores the high-order spectra (HOS) of the DD domain channels, called the HOS-BC algorithm. The simulation results indicate that the proposed algorithm not only improves the sensing resolution under the given bandwidth and frame time but also achieves a higher sensing accuracy at a low pilot signal-to-noise ratio compared with the A-BC algorithm.

关键词： Sensors Channel estimation Signal processing algorithms Delays OFDM Receivers Time-frequency analysis High order spectrum barycenter calibration orthogonal time frequency space integrated sensing and communication window design estimation error

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Closed-Form Model for Performance Analysis of THz Joint Radar-Communication Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第12期22卷 8694-8706页

作者： Liu, Zile Yang, Chuang Sun, Yanran Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

As an enabler technology in future network, terahertz (THz) joint radar-communication (T-JRC) has been envisioned as a solution to overcome the challenges in THz communications. Recent works on JRC mainly emphasize either performance tradeoffs or mutual benefits between radar and communication functionalities. Unable to characterize both benefit and tradeoff relationships, existing metrics and models are inapplicable to performance analysis on T-JRC systems. In this paper, a closed-form T-JRC performance model are proposed to explore the relationship between radar and communication metrics. To begin with, a novel T-JRC system model concerning random mobility and THz beam tracking is put forward. Next, the performance bounds of T-JRC performance in terms of communication rate and a proposed radar rate are acquired in three different integration cases. Furthermore, the expression of communication rate by radar rate in T-JRC is obtained theoretically, which is the first time to derive the closed-form relationship considering both benefits and tradeoffs to the best of our knowledge. Finally, numerical results validate the proposed model as well as closed-form expression, showing that the model succeeds in benefit and tradeoff relationship characterization in T-JRC systems and the fitting bounds are applicable in non-extreme scenarios.

关键词： Radar Measurement Radar tracking Wireless communication Antennas Transceivers Estimation Terahertz joint radar-communication close-form benefits and tradeoffs

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DeepRT: A Hybrid Framework Combining Large Model Architectures and Ray Tracing Principles for 6G Digital Twin Channels

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ELECTRONICS 2025年第9期14卷 1849-1849页

作者： Li, Mingyue Wu, Tao Dong, Zhirui Liu, Xiao Lu, Yiwen Zhang, Shuo Wu, Zerui Zhang, Yuxiang Yu, Li Zhang, Jianhua Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With the growing demand for wireless communication, the sixth-generation (6G) wireless network will be more complex. The digital twin channel (DTC) is envisioned as a promising enabler for 6G, as it can create an online replica of the physical channel characteristics in the digital world, thereby supporting precise and adaptive communication decisions for 6G. In this article, we systematically review and summarize the existing efforts in realizing the DTC, providing a comprehensive analysis of ray tracing (RT), artificial intelligence (AI), and large model approaches. Based on this analysis, we further explore the potential of integrating large models with RT methods. By leveraging the strong generalization, multi-task processing capabilities, and multi-modal fusion capabilities of large models while incorporating physical priors from RT as expert knowledge to guide their training, there is a strong possibility of fulfilling the fast online inference and precise mapping requirements of the DTC. Therefore, we propose a novel DeepRT-enabled DTC (DRT-DTC) framework, which combines physical laws with large models like DeepSeek, offering a new vision for realizing the DTC. Two case studies are presented to demonstrate the possibility of this approach, which validate the effectiveness of physical law-based AI methods and large models in generating the DTC.

关键词： digital twin channel ray tracing artificial intelligence deep ray tracing large models

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Federated Learning Meets Blockchain: state Channel-Based Distributed Data-Sharing Trust Supervision Mechanism

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IEEE INTERNET OF THINGS JOURNAL 2023年第14期10卷 12066-12076页

作者： Zhang, Fan Guo, Shaoyong Qiu, Xuesong Xu, Siya Qi, Feng Wang, Zhili Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With the rapid development of the 5G and 6G technology, it has become an inevitable trend to share the cross-domain scattered data and enhance data value transmission. As a new data-sharing technology with intelligence and privacy computing, federated learning (FL) receives wide attention. It can realize data value delivery and data privacy protection at the same time, however, it lacks supervision in the application process, and the reliability of the calculation process and result transmission cannot be guaranteed. As a distributed ledger technology, blockchain has the trust property but lacks computing power. Therefore, we propose to extend the computing and supervision capabilities of blockchain with state channel, using state channel to create sandboxes and instantiate FL tasks in order to realize the trust supervision mechanism based on sandboxes. In this article, we establish an FL-based distributed data-sharing architecture and on the basis of the architecture we design a state channel-based distributed data-sharing trust supervision mechanism. Through theoretical analysis and experimental verification, the supervision mechanism we designed has an excellent performance in improving system security, resisting malicious attacks, and improving data model quality.

关键词： Blockchain computing sandbox data sharing federated learning (FL) state channel trust supervision. domain user

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Graph attention network-optimized dynamic monocular visual odometry

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APPLIED INTELLIGENCE 2023年第20期53卷 23067-23082页

作者： Hongru, Zhao Xiuquan, Qiao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Monocular Visual Odometry (VO) is often formulated as a sequential dynamics problem that relies on scene rigidity assumption. One of the main challenges is rejecting moving objects and estimating camera pose in dynamic environments. Existing methods either take the visual cues in the whole image equally or eliminate the fixed semantic categories by heuristics or attention mechanisms. However, they fail to tackle unknown dynamic objects which are not labeled in the training sets of the network. To solve these issues, we propose a novel framework, named graph attention network (GAT)-optimized dynamic monocular visual odometry (GDM-VO), to remove dynamic objects explicitly with semantic segmentation and multi-view geometry in this paper. Firstly, we employ a multi-task learning network to perform semantic segmentation and depth estimation. Then, we reject priori known and unknown objective moving objects through semantic information and multi-view geometry, respectively. Furthermore, to our best knowledge, we are the first to leverage GAT to capture long-range temporal dependencies from consecutive image sequences adaptively, while existing sequential modeling approaches need to select information manually. Extensive experiments on the KITTI and TUM datasets demonstrate the superior performance of GDM-VO overs existing state-of-the-art classical and learning-based monocular VO.

关键词： Monocular visual odometry Multi-task learning Multi-view geometry Dynamic objects removal Graph attention network

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ATLAS: GAN-Based Differentially Private Multi-Party Data Sharing

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IEEE TRANSACTIONS ON BIG DATA 2023年第4期9卷 1225-1237页

作者： Wang, Zhenya Cheng, Xiang Su, Sen Liang, Jintao Yang, Haocheng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

In this article, we study the problem of differentially private multi-party data sharing, where the involved parties assisted by a semi-honest curator collectively generate a shared dataset while satisfying differential privacy. Inspired by the success of data synthesis with the generative adversarial network (GAN), we propose a novel GAN-based differentially private multi-party data sharing approach named ATLAS. In ATLAS, we extend the original GAN to multiple discriminators, and let each party hold a discriminator while the curator holds a generator. To update the generator without compromising each party's privacy, we decompose the calculation of the generator's gradient and selectively sanitize the discriminators' responses. Additionally, we propose two methods to improve the utility of shared data, i.e., the collaborative discriminator filtering (CDF) method and the adaptive gradient perturbation (AGP) method. Specifically, the CDF method utilizes trained discriminators to refine synthetic records, while the AGP method adaptively adjusts the noise scale during training to reduce the impact of deferentially private noise on the final shared data. Extensive experiments on real-world datasets validate the superiority of our ATLAS approach.

关键词： Differential privacy multiple parties data sharing generative adversarial network

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