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The Power of Bamboo: On the Post-Compromise security for Searchable Symmetric Encryption

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

A Cross-Domain Secure Deduplication Scheme Based on Threshold Blind Signature 9th

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A Cross-Domain Secure Deduplication Scheme Based on Threshol...

9th International Conference on Mobile Wireless Middleware, Operating Systems and Applications, MOBILWARE 2020

作者： Du, Jinlei Deng, Jide Qi, Hui Di, Xiaoqiang Jiang, Zhengang Jilin Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun China School of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (纸本)9783030622046

In the cloud storage environment, client-side deduplication can perform file repetitive detection locally. However, client-side deduplication still faces many security challenges. First, if the file hash value is used as evidence for repetitive detection, the attacker is likely to obtain the entire file information through the hash value of the file. Secondly, in order to protect data privacy, convergence encryption is widely used in the data deduplication scheme. Since the data itself is predictable, convergence encryption is still vulnerable to brute force attacks. In order to solve the above problems, this paper proposes to construct a secure deduplication scheme by using the threshold blind signature method. The generation of the convergence key is coordinated by multiple key servers, ensuring the confidentiality of the convergence key and effectively solving the violent dictionary attack problem. At the same time, since the key center is introduced to centrally manage the keys, the interaction between the key servers is reduced, and the key generation efficiency is improved. In addition, since the key server in this paper can be distributed in multiple independent network domains and interact with the key center through the Internet, the problem of cross-domain deduplication is solved. The experimental results show that the performance of this scheme is greatly improved in terms of system initialization and key generation. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Cryptography

FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning

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arXiv 2024年

作者： Sun, Dongwei Yao, Jing Zhou, Changsheng Cao, Xiangyong Ghamisi, Pedram School of Computer Science and Technology the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics and Statistics Guangdong University of Technology Guangzhou China Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany Lancaster Environment Centre Lancaster University LancasterLA1 4YR United Kingdom

Remote sensing image change description represents an innovative multimodal task within the realm of remote sensing processing. This task not only facilitates the detection of alterations in surface conditions, but also provides comprehensive descriptions of these changes, thereby improving human interpretability and interactivity. Generally, existing deep-learning-based methods predominantly utilized a three-stage framework that successively perform feature extraction, feature fusion, and localization from bitemporal images before text generation. However, this reliance often leads to an excessive focus on the design of specific network architectures and restricts the feature distributions to the dataset at hand, which in turn results in limited generalizability and robustness during application. To address these limitations, this paper proposes a novel approach for remote sensing image change detection and description that incorporates diffusion models, aiming to transition the emphasis of modeling paradigms from conventional feature learning to data distribution learning. The proposed method primarily includes a simple multi-scale change detection module, whose output features are subsequently refined by an well-designed diffusion model. Furthermore, we introduce a frequency-guided complex filter module to boost the model performance by managing high-frequency noise throughout the diffusion process. We validate the effectiveness of our proposed method across several datasets for remote sensing change detection and description, showcasing its superior performance compared to existing techniques. The code will be available at MaskApproxNet after a possible publication. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

A Hybrid Blockchain-Based Identity Authentication Scheme for Mobile Crowd Sensing

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SSRN

SSRN 2022年

作者： Taochun, Wang Huimin, Shen Jian, Chen Fulong, Chen Qingshan, Wu Dong, Xie School of Computer and Information Anhui Normal University Wuhu241002 China Anhui Engineering Research Centers of Medical Big Data Intelligent System Anhui Normal University Wuhu241002 China Anhui Key Laboratory of Network and Information Security Anhui Normal University Wuhu241002 China

With the continuous innovation and popularization of mobile smart devices, the application of Mobile Crowd Sensing (MCS) has been widely studied. However, the existing centralized MCS applications that use servers for task publishing and data collection have common problems such as a single point of failure and privacy protection. Based on this, we proposes a hybrid blockchain-based identity authentication scheme for MCS named HBIA, which uses blockchain technology to solve the single point failure problem of MCS. HBIA builds a cluster structure based on factors such as geographical location and balance, and uses this to build a hybrid blockchain, with the cluster head node and the node within the cluster authenticating on the public chain and the private chain respectively, and the use of Zero-knowledge proof(ZKP) to ensure the identity privacy of participants, thus balancing the contradiction between blockchain transparency and participant identity privacy. In addition, HBIA uses zero-knowledge succinct non-interactive argument of knowledge(zk-SNARKs) technology to implement off-chain computing and on-chain verification, further reducing the workload of the blockchain. Finally, the HBIA scheme was tested on the Ethereum public test network Ropsten, and the test results showed that the identity authentication scheme proposed in this paper has advantages in execution time and throughput. © 2022, The Authors. All rights reserved.

关键词： Smart contract

Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features

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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024, CC BY.

关键词： Semantics

Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning

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arXiv 2024年

作者： Zhou, Shicheng Liu, Jingju Lu, Yuliang Yang, Jiahai Zhang, Yue Chen, Jie College of Electronic Engineering National University of Defense Technology Hefei230037 China Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Anhui Province Hefei230037 China Institute for Network Sciences and Cyberspace The Beijing National Research Center for Information Science and Technology Tsinghua University Beijing10084 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

With increasing numbers of vulnerabilities exposed on the internet, autonomous penetration testing (pentesting) has emerged as a promising research area. Reinforcement learning (RL) is a natural fit for studying this topic. However, two key challenges limit the applicability of RL-based autonomous pentesting in real-world scenarios: (a) training environment dilemma – training agents in simulated environments is sample-efficient while ensuring their realism remains challenging;(b) poor generalization ability – agents’ policies often perform poorly when transferred to unseen scenarios, with even slight changes potentially causing significant generalization gap. To this end, we propose GAP, a generalizable autonomous pentesting framework that aims to realizes efficient policy training in realistic environments and train generalizable agents capable of drawing inferences about other cases from one instance. GAP introduces a Real-to-Simto-Real pipeline that (a) enables end-to-end policy learning in unknown real environments while constructing realistic simulations;(b) improves agents’ generalization ability by leveraging domain randomization and meta-RL learning. Specially, we are among the first to apply domain randomization in autonomous pentesting and propose a large language model-powered domain randomization method for synthetic environment generation. We further apply meta-RL to improve agents’ generalization ability in unseen environments by leveraging synthetic environments. The combination of two methods effectively bridges the generalization gap and improves agents’ policy adaptation performance. Experiments are conducted on various vulnerable virtual machines, with results showing that GAP can enable policy learning in various realistic environments, achieve zero-shot policy transfer in similar environments, and realize rapid policy adaptation in dissimilar environments. The source code is available at https://***/Joe-zsc/GAP. Copyright © 2024, The Auth

关键词： Reinforcement learning

Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

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Safety Guaranteed Power-Delivered-to-Load Maximization for M...

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

Collaborative Authentication for 6G networks: An Edge Intelligence based Autonomous Approach

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arXiv 2023年

作者： Fang, He Xiao, Zhenlong Wang, Xianbin Xu, Li Hanzo, Lajos The School of Electronic and Information Engineering Soochow University Soochow215301 China The Department of Informatics and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada The College of Computer and Cyber Security The Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou350117 China School of Electronics and Computer Science University of Southampton SO17 1BJ United Kingdom

The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. They also tend to ignore attacker localization in the collaborative authentication process. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to assist the service provider in distributively authenticating its users by estimating their received signal strength indicator (RSSI) and mobility trajectory (TRA). More explicitly, a distributed learning-based collaborative authentication algorithm is conceived, where the cooperative peers update their authentication models locally, thus the network congestion and response time remain low. Moreover, a situation-aware secure group update algorithm is proposed for autonomously refreshing the set of cooperative peers in the dynamic environment. We also develop an algorithm for localizing a malicious user by the cooperative peers once it is identified. The simulation results demonstrate that the proposed scheme is eminently suitable for both indoor and outdoor communication scenarios, and outperforms some existing benchmark schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Authentication