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ECAS: an efficient and conditional privacy preserving collision warning system in fog-based vehicular ad hoc networks

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CCF Transactions on networking 2020年第3期3卷 205-217页

作者： Qin, Zhiguang Li, Yuedi Ye, Xin Zhou, Jin Cao, Minsheng Chen, Dajiang Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Peng Cheng Laboratory Shenzhen China

VANET is a prominent way to provide road security and prevent vehicles from collision by using various methods, such as message dissemination, traffic management, etc. However, the traditional vehicular network faces some problems related to high latency, low bandwidth, and communication in open wireless environment. Thus, some researchers have attempted to combine fog computing with vehicular ad hoc networks to overcome these problems. In this paper, we design an efficient and conditional privacy preserving collision warning system for fog-based vehicular ad hoc networks without using bilinear pairing. The fog nodes collect the speed violation reports from the speed sensor of vehicles. This protocol achieves privacy protection, message authentication, and revocate malicious vehicles. We also provide strict security proof and illustrate how to reach the security requirements in the proposed protocol. Moreover, the experiment demonstrates that the proposed protocol provides better efficiency in computation overhead and communication overhead, and makes it more applicable for adoption in the VANET collision warning systems.

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Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images

arXiv

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arXiv 2023年

作者： Wang, Yuntao Kong, Yan Yan, Yukang Cheng, Zirui Wang, Xueyang Yu, Chun Shi, Yuanchun Yi, Xin Xu, Xuhai Patel, Shwetak Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Beijing China Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Qinghai University Qinghai Xining China Zhongguancun Laboratory Beijing China Information School University of Washington SeattleWA United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States

A computer vision system using low-resolution image sensors can provide intelligent services (e.g., activity recognition) but preserve unnecessary visual privacy information from the hardware level. However, preserving visual privacy and enabling accurate machine recognition have adversarial needs on image resolution. Modeling the trade-off of privacy preservation and machine recognition performance can guide future privacy-preserving computer vision systems using low-resolution image sensors. In this paper, using the at-home activity of daily livings (ADLs) as the scenario, we first obtained the most important visual privacy features through a user survey. Then we quantified and analyzed the effects of image resolution on human and machine recognition performance in activity recognition and privacy awareness tasks. We also investigated how modern image super-resolution techniques influence these effects. Based on the results, we proposed a method for modeling the trade-off of privacy preservation and activity recognition on low-resolution images. © 2023, CC BY-NC-SA.

关键词： Image resolution

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An Improved CTU-level Rate Control Algorithm Based on Temporal Domain Motion Intensity

An Improved CTU-level Rate Control Algorithm Based on Tempor...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Shaokang Wang Songlin Sun Jiaqi Zou Ruotong Wu Zhilei Ling Xingtong Liu School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK Business School Beijing Normal University Beijing China

Rate control (RC) plays a vital role in Versatile Video Coding (VVC) and video transmission. However, the RC algorithm in VVC remains the problem that the process of updating model parameters only considers the impact of the current encoding block content, without considering the similarity and reference value between different Coding Tree Units (CTUs) in the temporal domain. As a result, the coding performance is not optimal. To address this problem and further optimize the RC of VVC, this paper proposes an improved CTU-level rate control model parameter updating algorithm based on temporal domain motion intensity and we integrate this algorithm into VTM 1S.0. Experiments show that the proposed algorithm achieves better coding performance than anchor with 0.74% and 1.03% BD-rate saving under the Low Delay B (LDB) and Random Access (RA) configurations, respectively, while obtaining smaller bitrate error under common test conditions.

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Graph-Based Terminal Ranking for Sparsification of Interference Coordination Parameters in Ultra-Dense networks 10th

Graph-Based Terminal Ranking for Sparsification of Interfere...

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10th EAI International Conference on Mobile networks and Management, MONAMI 2020

作者： Gou, Junxiang Wang, Lusheng Lin, Hai Peng, Min Ministry of Education School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230601 China Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan University Wuhan430072 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230601 China

ISBN: (纸本)9783030640019

In the future mobile communication system, inter-cell interference becomes a serious problem due to the intensive deployment of cells and terminals. Traditional interference coordination schemes take long time for optimization in ultra-dense networks. Meanwhile, due to the increase of factors affecting communication and in order to better meet the communication needs of each terminal, an interference coordination scheme needs to fully consider multiple characteristic parameters of the terminal, which will further increase the scheme’s computational time. Therefore, we should compress all the data through sparsification of parameters before optimization. There are many terminal parameters, and the essence of sparsification of parameters is to rank terminals. In this paper, a graph-based terminal ranking scheme is designed. First, each terminal can be represented by its multiple parameters. Then, all terminals are used as the vertexes in the graph to form a complete weighted graph, and edge weights represent the degree of dissimilarity between terminals. A ranking of terminals is obtained by finding a minimum Hamiltonian path in the graph. Finally, the ranking of all parameter sequences is obtained according to terminals ranking, which makes the sparsity of all parameter sequences better. Simulation results show that the proposed scheme can accomplish sparsification of parameter sequences effectively, especially when the number of sequences increases. In addition, compared with the optimal coordination of traditional scheme, this scheme improves the fairness of the system while ensuring high system capacity, and dramatically reduces the computational time of interference coordination. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.

关键词： Approximation algorithms

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ImLiDAR: Cross-Sensor Dynamic Message Propagation network for 3D Object Detection

arXiv

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arXiv 2022年

作者： Shen, Yiyang Yu, Rongwei Wu, Peng Xie, Haoran Gong, Lina Qin, Jing Wei, Mingqiang The Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Department of Computing and Decision Sciences Lingnan University Hong Kong Hong Kong The School of Nursing The Polytechnic University of Hong Kong Hong Kong Hong Kong

LiDAR and camera, as two different sensors, supply geometric (point clouds) and semantic (RGB images) information of 3D scenes. However, it is still challenging for existing methods to fuse data from the two cross sensors, making them complementary for quality 3D object detection (3OD). We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multi-scale features of camera Images and LiDAR point clouds. ImLiDAR enables to provide the detection head with cross-sensor yet robustly fused features. To achieve this, two core designs exist in ImLiDAR. First, we propose a cross-sensor dynamic message propagation module to combine the best of the multi-scale image and point features. Second, we raise a direct set prediction problem that allows designing an effective set-based detector to tackle the inconsistency of the classification and localization confidences, and the sensitivity of hand-tuned hyperparameters. Besides, the novel set-based detector can be detachable and easily integrated into various detection networks. Comparisons on both the KITTI and SUN-RGBD datasets show clear visual and numerical improvements of our ImLiDAR over twenty-three state-of-the-art 3OD methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Self-supervised GANs with label augmentation 21

Self-supervised GANs with label augmentation

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Liang Hou Huawei Shen Qi Cao Xueqi Cheng Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences

ISBN: (纸本)9781713845393

Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environment. However, the separate self-supervised tasks in existing self-supervised GANs cause a goal inconsistent with generative modeling due to the fact that their self-supervised classifiers are agnostic to the generator distribution. To address this problem, we propose a novel self-supervised GAN that unifies the GAN task with the self-supervised task by augmenting the GAN labels (real or fake) via self-supervision of data transformation. Specifically, the original discriminator and self-supervised classifier are unified into a label-augmented discriminator that predicts the augmented labels to be aware of both the generator distribution and the data distribution under every transformation, and then provide the discrepancy between them to optimize the generator. Theoretically, we prove that the optimal generator could converge to replicate the real data distribution. Empirically, we show that the proposed method significantly outperforms previous self-supervised and data augmentation GANs on both generative modeling and representation learning across benchmark datasets.

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From proof-of-concept to exploitable

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Cybersecurity 2019年第1期2卷 189-213页

作者： Yan Wang WeiWu Chao Zhang Xinyu Xing Xiaorui Gong Wei Zou School of Cyber Security University of Chinese Academy of SciencesBeijingChina Institute for Network Sciences and Cyberspace Tsinghua UniversityBeijingChina College of Information Sciences and Technology Pennsylvania State UniversityUniversity ParkUnited States Institute of Information Engineering Chinese Academy of SciencesBeijingChina Key Laboratory of Network Assessment Technology CASBeijingChina Beijing Key Laboratory of Network Security and Protection Technology BeijingChina

Exploitability assessment of vulnerabilities is important for both defenders and *** ultimate way to assess the exploitability is crafting a working ***,it usually takes tremendous hours and significant manual *** address this issue,automated techniques can be *** solutions usually explore in depth the crashing paths,i.e.,paths taken by proof-of-concept(PoC)inputs triggering vulnerabilities,and assess exploitability by finding exploitable states along the ***,exploitable states do not always exist in crashing ***,existing solutions heavily rely on symbolic execution and are not scalable in path exploration and exploit *** this paper,we propose a novel solution to generate exploit for userspace programs or facilitate the process of crafting a kernel UAF ***,we utilize oriented fuzzing to explore diverging paths from vulnerability *** userspace programs,we adopt a control-flow stitching solution to stitch crashing paths and diverging paths together to generate *** kernel UAF,we leverage a lightweight symbolic execution to identify,analyze and evaluate the system calls valuable and useful for exploiting *** have developed a prototype system and evaluated it on a set of 19 CTF(capture the flag)programs and 15 realworld Linux kernel UAF *** results showed it could generate exploit for most of the userspace test set,and it could also facilitate security mitigation bypassing and exploitability evaluation for kernel test set.

关键词： Exploit Vulnerability Taint analysis Fuzzing Symbolic execution

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ms-PoSW: A multi-server aided proof of shared ownership scheme for secure deduplication in cloud

ms-PoSW: A multi-server aided proof of shared ownership sche...

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作者： Xiong, Jinbo Zhang, Yuanyuan Lin, Li Shen, Jian Li, Xuan Lin, Mingwei College of Mathematics and Informatics Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou350117 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China

Collaborative cloud applications have become the dominant application mode in the big data era. These applications usually generate plenty of cooperative files, which share their ownerships with all collaborative participants. Data deduplication is a promising solution to improve the storage efficiency and save the user expenditure. However, it remains an open issue on how to securely prove the shared ownerships for the shared files and address the attacks on account of using data deduplication. To tackle the above issue, in this paper, we introduce a novel concept of the Proof of Shared oWnership (PoSW) and construct a secure multi-server-aided PoSW (ms-PoSW) scheme for securing client-side deduplication for the shared files, which is based on the convergent encryption, secret sharing, and bloom filter. In the ms-PoSW scheme, we employ a sharing convergent key to avoid the single point of failure, introduce the secret sharing algorithm to implement the shared ownership, and construct a novel interaction protocol between the shared owners and the cloud server to prove the shared ownership. Furthermore, a hybrid PoSW scheme is constructed to address the secure proof of hybrid cloud architectures. Finally, security analysis and performance evaluation show the security and efficiency of the proposed schemes. © 2017 John Wiley & Sons, Ltd.

关键词： Side channel attack

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Acq: Improving Generative Data-Free Quantization Via Attention Correction

SSRN

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SSRN 2023年

作者： Li, Jixing Guo, Xiaozhou Dai, Benzhe Gong, Guoliang Jin, Min Chen, Gang Mao, Wenyu Lu, Huaxiang Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China University of Chinese Academy of Sciences Beijing100089 China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Laboratory Beijing100083 China Materials and Optoelectronics Research Center University of Chinese Academy of Sciences Beijing100083 China College of Microelectronics University of Chinese Academy of Sciences Beijing China

Data-free quantization aims to achieve model quantization without accessing any authentic sample. It is significant in an application-oriented context involving data privacy. Converting noise vectors into synthetic samples through a generator is a popular data-free quantization method, which is called generative data-free quantization. However, there is a difference in attention between synthetic samples and authentic samples. This is always ignored and restricts the quantization performance. First, since synthetic samples of the same class are prone to have homogenous attention, the quantized network can only learn limited modes of attention. Second, synthetic samples in eval mode and training mode exhibit different attention. Hence, the batch-normalization statistics matching tends to be inaccurate. ACQ is proposed in this paper to fix the attention of synthetic samples. An attention center position-condition generator is established regarding the homogenization of intra-class attention. Restricted by the attention center matching loss, the attention center position is treated as the generator’s condition input to guide synthetic samples in obtaining diverse attention. Moreover, we design adversarial loss of paired synthetic samples under the same condition to prevent the generator from paying overmuch attention to the condition, which may result in mode collapse. To improve the attention similarity of synthetic samples in different network modes, we introduce a consistency penalty to guarantee accurate BN statistics matching. The experimental results demonstrate that ACQ effectively improves the attention problems of synthetic samples. Under various training settings, ACQ achieves the best quantization performance. For the 4-bit quantization of Resnet18 and Resnet50, ACQ reaches 67.55% and 72.23% accuracy, respectively. © 2023, The Authors. All rights reserved.

关键词： Data privacy

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VEIN: High Scalability Routing Algorithm for Blockchain-based Payment Channel networks

VEIN: High Scalability Routing Algorithm for Blockchain-base...

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IEEE International Conference on Trust, security and Privacy in computing and Communications (TrustCom)

作者： Qianyun Gong Chengjin Zhou Le Qi Jianbin Li Jianzhong Zhang Jingdong Xu Institute of Systems and Networks College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Shijiazhuang Huayan Traffic Technology Co. Ltd.

ISBN: (纸本)9781665416597

The payment channel networks (PCNs), as the main method of blockchain off-chain expansion, have received extensive attention in recent years. Using the PCNs, two unconnected parties in a transaction can forward payments through existing payment channels of other nodes, which dramatically reduces interactions with the blockchain. But in large-scale dynamic PCNs, the routing mechanism is a challenge. Existing PCNs routing algorithms have some limitations. The landmark routing destroys the decentralization of blockchain. The static routing requires nodes having a global view and ultra-high computing power, which can not be applied to light nodes. We propose VEIN, a dynamic multi-path source routing algorithm, which is suitable for not only full nodes but also light nodes. We present an ingenious routing protocol, a modified max-flow algorithm to find edge-disjoint paths, and a path selection algorithm to deal with the NP-hard multi-path selection problem. Extensive experiments show that VEIN increases the transaction success ratio by 34% with the state-of-art algorithm, and realizes multiple orders of magnitude reduction in storage. In addition, we implement a prototype of VEIN on the Ethereum testnet to verify its feasibility.

关键词： Privacy Heuristic algorithms Veins Scalability Conferences Prototypes Routing

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