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

作者： Ning Xu Congyu Qiao Xin Geng Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and MOE Key Laboratory of Computer Network and Information Integration Ministry of Education China

ISBN: (纸本)9781713845393

Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels. However, this assumption is not realistic since the candidate labels are always instance-dependent. In this paper, we consider instance-dependent PLL and assume that each example is associated with a latent label distribution constituted by the real number of each label, representing the degree to each label describing the feature. The incorrect label with a high degree is more likely to be annotated as the candidate label. Therefore, the latent label distribution is the essential labeling information in partially labeled examples and worth being leveraged for predictive model training. Motivated by this consideration, we propose a novel PLL method that recovers the label distribution as a label enhancement (LE) process and trains the predictive model iteratively in every epoch. Specifically, we assume the true posterior density of the latent label distribution takes on the variational approximate Dirichlet density parameterized by an inference model. Then the evidence lower bound is deduced for optimizing the inference model and the label distributions generated from the variational posterior are utilized for training the predictive model. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed method.

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A Private Statistic Query Scheme for Encrypted Electronic Medical Record System *

A Private Statistic Query Scheme for Encrypted Electronic Me...

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International Conference on computer Supported Cooperative Work in Design

作者： Xianxian Li Xuemei Fu Feng Yu Zhenkui Shi Jie Li Junhao Yang Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

In this paper, we propose a scheme that supports statistic query and authorized access control on an Encrypted Electronic Medical Records Databases(EMDB). Different from other schemes, it is based on Differential-Privacy(DP), which can protect the privacy of patients. By deploying an improved Multi-Authority Attribute-Based Encryption(MA-ABE) scheme, all authorities can distribute their search capability to clients under different authorities without additional negotiations. To our best knowledge, there are few studies on statistical queries on encrypted data. In this work, we consider that support differentially-private statistical queries. To improve search efficiency, we leverage the Bloom Filter(BF) to judge whether the keywords queried by users exists. Finally, we use experiments to verify and evaluate the feasibility of our proposed scheme.

关键词： Access control Data privacy Privacy Histograms Databases Conferences Cryptography

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Cone-Beam Computed Tomography Limited-Angle Image Reconstruction Using Planning CT-Guided Deep Diffusion Model

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Wu, Zhan Yang, Yang Hu, Dianlin Lyu, Tianling Li, Yinsheng Tang, Hao Shu, Huazhong Xi, Yan Zhang, Yi Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Zhejiang University of Technology Academy for Advanced Interdisciplinary Science and Technology Hangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System China Capital Medical University Beijing Jishuitan Hospital China First-Imaging Tech Shanghai China Sichuan University School of Cyber Science and Engineering Chengdu China Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing210096 China

For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and linear accelerator configurations. However, the limited-angle CBCT reconstruction under incomplete projection data causes severe artifacts in the reconstructed images, further having a negative impact on the registration accuracy in IGRT. In this paper, we propose a planning CT-guided deep diffusion model, termed LARGE-DiffNet, to improve imaging quality and registration accuracy for limited-angle CBCT image reconstruction. In stark contrast to existing approaches leveraging hand-crafted regularization, such as local smoothness and/or sparsity of the image, LARGE-DiffNet learns the distribution-level prior knowledge to describe the features of ideal CBCT images via a planning CT-guided diffusion model. The proposed LARGE-DiffNet is evaluated on the clinical CBCT dataset, and extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. The proposed LARGE-DiffNet technique has great potential in IGRT. © 1963-2012 IEEE.

关键词： cone-beam computed tomography deep learning diffusion model image registration image-guided radiation therapy limited-angle

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BEIR:A Blockchain-based Encrypted Image Retrieval Scheme

BEIR:A Blockchain-based Encrypted Image Retrieval Scheme

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International Conference on computer Supported Cooperative Work in Design

作者： Xianxian Li Jie Li Feng Yu Xuemei Fu JunHao Yang Yue Chen Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

ISBN: (纸本)9781728165974;9781728165981

Encrypted image retrieval may return incorrect or incomplete results due to threats from malicious cloud servers. Most of the existing solutions focus on the efficiency and accuracy of retrieval, lack of verification of the completeness of search results, to achieve the reliability of search results and the transparency of the search process, we explore characteristic such as the decentralization and tamper-proof of blockchain, proposed a blockchain-based encrypted image retrieval scheme. This scheme stores the encrypted index on the blockchain (Ethereum), through the blockchain consensus mechanism and the function of searching on the smart contract, ensures the integrity and correctness of search results, then outsources the corresponding encrypted images to the cloud server to reduce storage cost, and designs a double-layer index structure using the bag of visual word model and simhash in the process of image similarity index. Experiments show that the reliability, high retrieval efficiency, and precision of the scheme also have a good privacy protection effect.

关键词： Privacy Visualization Image retrieval Smart contracts Blockchain Search problems Encryption

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PPBFL: A Privacy Protected Blockchain-based Federated Learning Model

arXiv

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arXiv 2024年

作者： Li, Yang Xia, Chunhe Lin, Wanshuang Wang, Tianbo The Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China The Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China The School of Cyber Science and Technology Beihang University Beijing100191 China The Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

With the rapid development of machine learning and a growing concern for data privacy, federated learning has become a focal point of attention. However, attacks on model parameters and a lack of incentive mechanisms hinder the effectiveness of federated learning. Therefore, we propose A Privacy Protected Blockchain-based Federated Learning Model (PPBFL) to enhance the security of federated learning and encourage active participation of nodes in model training. Blockchain technology ensures the integrity of model parameters stored in the InterPlanetary File System (IPFS), providing protection against tampering. Within the blockchain, we introduce a Proof of Training Work (PoTW) consensus algorithm tailored for federated learning, aiming to incentive training nodes. This algorithm rewards nodes with greater computational power, promoting increased participation and effort in the federated learning process. A novel adaptive differential privacy algorithm is simultaneously applied to local and global models. This safeguards the privacy of local data at training clients, preventing malicious nodes from launching inference attacks. Additionally, it enhances the security of the global model, preventing potential security degradation resulting from the combination of numerous local models. The possibility of security degradation is derived from the composition theorem. By introducing reverse noise in the global model, a zero-bias estimate of differential privacy noise between local and global models is achieved. Furthermore, we propose a new mix transactions mechanism utilizing ring signature technology to better protect the identity privacy of local training clients. Security analysis and experimental results demonstrate that PPBFL, compared to baseline methods, not only exhibits superior model performance but also achieves higher security. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

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No Place to Hide: Dual Deep Interaction Channel network for Fake News Detection based on Data Augmentation

arXiv

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arXiv 2023年

作者： Cao, Biwei Hua, Lulu Cao, Jiuxin Gui, Jie Liu, Bo Kwok, James Tin-Yau The School of Cyber Science and Engineering Southeast University Nanjing211189 China The Key Laboratory of Computer Network and Information of Ministry of Education of China Nanjing211189 China The School of Computer Science and Engineering Southeast University Nanjing211189 China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology 999077 Hong Kong

Online Social network (OSN) has become a hotbed of fake news due to the low cost of information dissemination. Although the existing methods have made many attempts in news content and propagation structure, the detection of fake news is still facing two challenges: one is how to mine the unique key features and evolution patterns, and the other is how to tackle the problem of small samples to build the high-performance model. Different from popular methods which take full advantage of the propagation topology structure, in this paper, we propose a novel framework for fake news detection from perspectives of semantic, emotion and data enhancement, which excavates the emotional evolution patterns of news participants during the propagation process, and a dual deep interaction channel network of semantic and emotion is designed to obtain a more comprehensive and fine-grained news representation with the consideration of comments. Meanwhile, the framework introduces a data enhancement module to obtain more labeled data with high quality based on confidence which further improves the performance of the classification model. Experiments show that the proposed approach outperforms the state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： information dissemination

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Efficient Registered Attribute Based Access Control with Same Sub-policies in Mobile Cloud Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Weng, Wuwei Li, Jiguo Zhang, Yichen Lu, Yang Shen, Jian Han, Jinguang Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Nanjing Normal University School of Computer and Electronic Information Nanjing210023 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China Southeast University School of Cyber Science and Engineering Nanjing210096 China

Ciphertext-policy attribute-based encryption (CP-ABE) has long been considered as a promising access control technology for cloud storage. However, CP-ABE depends on a central trusted authority to generate and distribute decryption keys, resulting in the key escrow issue. Most existing solutions only mitigate this problem but fail to resolve it entirely. Registered attribute-based encryption (RABE), a new cryptographic primitive, fundamentally addresses the key escrow problem by modifying the trust model, but its high computational overhead limits its practical application. Inspired by this challenge, we present an efficient registered attribute-based access control scheme designed for data encrypted with access policies containing the same sub-policy. In our scheme, users generate their own keys, while a key manager, who does not hold keys, replaces the central authority in managing users. Additionally, for data encrypted with the same sub-policy, the user's initial decryption stores the relevant parameters, which can be used for subsequent decryptions to reduce computational overhead. The proposed scheme is proven to achieve semantic security. Performance analysis demonstrates that our scheme enhances decryption efficiency by roughly 41.4% compared to existing RABE scheme, with a minimal storage trade-off, making it more practical for cloud storage application. © 2002-2012 IEEE.

关键词： Ciphertext

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Mining Latent Classes for Few-shot Segmentation

Mining Latent Classes for Few-shot Segmentation

引用

International Conference on computer Vision (ICCV)

作者： Lihe Yang Wei Zhuo Lei Qi Yinghuan Shi Yang Gao State Key Laboratory for Novel Software Technology Nanjing University Tencent Key Lab of Computer Network and Information Integration (Ministry of Education) Southeast University

ISBN: (纸本)9781665428132

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e., potential novel classes are treated as background during training phase. Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. Based on conventional episodic training on support-query pairs, we introduce an additional mining branch that exploits latent novel classes via transferable sub-clusters, and a new rectification technique on both background and fore-ground categories to enforce more stable prototypes. Over and above that, our transferable sub-cluster has the ability to leverage extra unlabeled data for further feature enhancement. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms previous state-of-the-art by a large margin of 3.7% mIOU on PASCAL-5 i and 7.0% mIOU on COCO-20 i at the cost of 74% fewer parameters and 2.5x faster inference speed. The source code is available at https://***/LiheYoung/MiningFSS.

关键词： Training computer vision Costs Codes Training data Prototypes Benchmark testing

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A Revocable Pairing-Free Certificateless Signature Scheme Based on RSA Accumulator

A Revocable Pairing-Free Certificateless Signature Scheme Ba...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhuowei Shen Xiao Kou Taiyao Yang Haoqin Xu Dongbin Wang Shaobo Niu School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Intergration Minster of Education Nanjing China School of Cyberspace Security Beijing University of Post and Telecommunications Beijing China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Certificateless public key cryptography (CL-PKC) has garnered significant attention in recent years due to its ability to address the complex certificate management requirements inherent in public key infrastructure (PKI) systems and the key escrow issue associated with identity-based cryptography (IBC). However, the efficient revocation of illegitimate users in authentication schemes based on CL-PKC remains a challenge. Moreover, certain schemes still exhibit security vulnerabilities, such as susceptibility to key replacement attacks, and also require performance enhancements. In this article, we propose a novel certificateless signature scheme that incorporates the RSA accumulator. By leveraging the witness in the RSA accumulator to produce the public keys of users, the authenticity of the public key can be efficiently verified, thereby eliminating key replacement attacks. Additionally, by removing specific elements from the RSA accumulator and broadcasting the update credential to legitimate users via a public channel, our proposed scheme allows the effective revocation of malicious users. Security analysis and performance evaluations indicate that our scheme provides comprehensive security and achieves high performance in terms of computational and communication costs in both signingverification and revocation processes.

关键词： Performance evaluation Privacy Costs Identity-based encryption Authentication Broadcasting Forgery Encryption Security Digital signatures

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Retrieve, program, repeat: Complex knowledge base question answering via alternate meta-learning 29

Retrieve, program, repeat: Complex knowledge base question a...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Hua, Yuncheng Li, Yuan-Fang Haffari, Gholamreza Qi, Guilin Wu, Wei School of Computer Science and Engineering Southeast University Nanjing China Faculty of Information Technology Monash University Melbourne Australia Southeast University Monash University Joint Research Institute Suzhou China Key Laboratory of Computer Network and Information Integration Southeast University China

ISBN: (纸本)9780999241165

A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka the programmer-interpreter approach. Use similar training questions to the test question, meta-learning enables the programmer to adapt to unseen questions to tackle potential distributional biases quickly. However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive. In this paper, we present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision, i.e., the system's performance with respect to the produced answers. To the best of our knowledge, this is the first attempt to train the retrieval model with the programmer jointly. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases. We have released our code at https://***/DevinJake/MARL. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Knowledge based systems

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