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Privacy-Preserving Edge-Aided Eigenvalue Decomposition in Internet of Things
Xiaotong Zhao;Hanlin Zhang;Jie Lin;Fan Liang;Fanyu Kong;Hansong Xu;Kun Hua
IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
Eigenvalue decomposition (EVD) is a fundamental yet time-consuming operation with extensive applications in IoT. When the matrix dimension reaches millions, resource-limited IoT devices struggle to perform such computationally expensive operations. Edge computing, with its plentiful computing resources, offers an effective solution to this problem. However, privacy concerns arise because outsource...Show More
How to Securely Outsource the Multiple Kernel Fuzzy Clustering Task in Edge Computing
Xinrong Sun;Yunting Tao;Fanyu Kong;Qian Yu;Chunpeng Ge;Qiuliang Xu;Hanlin Zhang
IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
For the huge amount of data from the Internet of Things (IoT) devices, multiple kernel learning is a widely concerned issue in data analyzing, among which the multiple kernel fuzzy clustering (MKFC) algorithm is an effective approach for extracting linear features in high-dimensional space. For a time-consuming multiple kernel clustering task, it’s meaningful to find a secure and efficient outsour...Show More
Adaptive Chosen-Plaintext Deep-Learning-Based Side-Channel Analysis
Yanbin Li;Yuxin Huang;Yikang Guo;Chunpeng Ge;Fanyu Kong;Yongjun Ren
Profiled side-channel analysis presents a significant risk to embedded devices in Internet of Things (IoT). Typically, a single trace is insufficient to successfully key recovery in practical scenarios. It still requires several traces based on Bayes’ posterior probability. In this article, we introduce a chosen-plaintext (CP) strategy into the deep learning-based profiled attacks to improve the a...Show More
Privacy-Preserving Group Closeness Maximization
Zihao Zhao;Sijia Cao;Hanlin Zhang;Jie Lin;Fanyu Kong;Leyun Yu
2024 33rd International Conference on Computer Communications and Networks (ICCCN)
Year: 2024 | Conference Paper |
This study explores the metric of group closeness centrality within the framework of social networks, a departure from the traditional analysis focused solely on the significance of individual nodes. Given the intricate dynamics observed in networks governed by various stakeholders, we introduce a framework that preserves privacy through the application of a greedy algorithm. This approach is desi...Show More
Privacy-Preserving Edge Assistance for Solving Matrix Eigenvalue Problem
2024 33rd International Conference on Computer Communications and Networks (ICCCN)
Year: 2024 | Conference Paper |
The large-scale matrix eigenvalue computation, as a basic mathematical tool, has been widely used in many fields such as face recognition and data analysis. However, local terminal devices lack sufficient resources to undertake heavy computational tasks, which poses a challenge to the applications of eigenvalue computation. In this paper, we propose the first privacy-preserving edge-assisted compu...Show More
Privacy-preserving Deep Learning for Autism Spectrum Disorder Classification
Guangmao Gao;Hanlin Zhang;Jie Lin;Hansong Xu;Fanyu Kong;Leyun Yu
2024 9th IEEE International Conference on Smart Cloud (SmartCloud)
Year: 2024 | Conference Paper |
Autism Spectrum Disorder (ASD) encompasses a range of complex neurodevelopmental conditions typically identified in early childhood. ASD is characterized by challenges in social interaction, communication, and by repetitive behaviors with restricted interests. The variability in symptoms’ severity and expression among individuals presents significant diagnostic challenges to physicians. Advancemen...Show More
Accelerating Graph Embedding Through Secure Distributed Outsourcing Computation in Internet of Things
Pengyu Cui;Yunting Tao;Bin Zhen;Fanyu Kong;Chunpeng Ge;Chuan Ma;Jia Yu
With the advancement of the Internet of Things (IoT), numerous machine learning applications on IoT are encountering performance bottlenecks. Graph embedding is an emerging type of machine learning that has achieved commendable results in areas, such as network anomaly detection, malware detection, IoT device management, and service recommendation within the IoT. However, for some resource-constra...Show More
Secure Outsourcing Evaluation for Sparse Decision Trees
Decision tree classifiers are pervasively applied in a wide range of areas, such as healthcare, credit-risk assessment, spam detection, and many more. To ensure effectiveness and efficiency, clients usually choose to adopt classification services that are offered by model providers. However, the required data interactions in the evaluation process raise privacy concerns for both the provider and t...Show More
Test Case Level Predictive Mutation Testing Combining PIE and Natural Language Features
Rui Xu;Yuliang Shi;Zhiyuan Su;Xinjun Wang;Zhongmin Yan;Fanyu Kong
2023 30th Asia-Pacific Software Engineering Conference (APSEC)
Year: 2023 | Conference Paper |
Approaches predicting the results of mutation testing by machine learning have been proposed to reduce the cost of mutation testing. The predictive approaches based on PIE theory and approaches based on natural language have been proposed. However, both PIE-based and natural language-based approaches have disadvantages, leading to a reduction in effectiveness at the test case level prediction. In ...Show More
FSFP: A Fine-Grained Online Service System Performance Fault Prediction Method Based on Cross-attention
Nanfei Yang;Yuliang Shi;Zhiyuan Su;Xinjun Wang;Zhongmin Yan;Fanyu Kong
2023 30th Asia-Pacific Software Engineering Conference (APSEC)
Year: 2023 | Conference Paper |
An online service system may experience various performance faults during operation. Detecting and locating these faults after they occur can significantly impact the user experience and lead to significant losses. Therefore, it is necessary to predict faults before they occur. Existing methods for fault prediction typically only predict the possibility of fault, without providing more granular pr...Show More
Secure parallel Outsourcing Scheme for Large-scale Matrix Multiplication on Distributed Cloud Servers
Yinlong Wang;Yunting Tao;Fanyu Kong;Zhaoquan Gu;Jia Yu;Hanlin Zhang
2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)
Year: 2023 | Conference Paper |
Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n3) for matrix multiplication, large matrix computation is exceedingly time-consuming for the client-side user. By outsourcing this task to cloud servers with substantial computational resources, we can significantly reduce t...Show More
Privacy-Preserving Deep Learning for Erythemato-Squamous Disease Classification
2023 IEEE Smart World Congress (SWC)
Year: 2023 | Conference Paper |
Erythemato-squamous disease (ESD) is a benign skin condition with a broad spectrum of symptoms, creating diagnostic challenges for physicians. Numerous studies have suggested the use of deep learning for constructing classification models. However, the patient data utilized for model training and inference is highly confidential, and any breach could lead to severe implications. To address this is...Show More
FedADP: Communication-Efficient by Model Pruning for Federated Learning
Haiyang Liu;Yuliang Shi;Zhiyuan Su;Kun Zhang;Xinjun Wang;Zhongmin Yan;Fanyu Kong
GLOBECOM 2023 - 2023 IEEE Global Communications Conference
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Federated learning is a new type of artificial intelligence technology. During the training process, the client transmits model parameter information instead of local data to ensure their privacy and security. But it also incurs higher communication costs. This article proposes a new federated learning pruning method, FedADP, with the aim of adaptively determining pruning ratios for each layer in ...Show More
A Collaborative Cross-Attention Drug Recommendation Model Based on Patient and Medical Relationship Representations
Yourong Li;Yuliang Shi;Yide Jin;Xinjun Wang;Zhongmin Yan;Zhiyong Chen;Fanyu Kong
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The purpose of drug recommendation is to predict the effective and safe drug combinations required for the current visit based on the historical medical data of patients. How to better mine the hidden relationship in the medical data and effectively improve the accuracy of drug recommendation are research hotspots in the medical field. This paper proposes a Collaborative Cross-attention Drug Recom...Show More
SecureGAN: Secure Three-Party GAN Training
Sijia Cao;Hanlin Zhang;Yuhang Wang;Jie Lin;Fanyu Kong;Leyun Yu
2023 32nd International Conference on Computer Communications and Networks (ICCCN)
Year: 2023 | Conference Paper |
Generating Adversarial Network (GAN) is a prominent unsupervised learning method that utilizes two competing neural networks to generate realistic data, which has been widely employed in image synthesis and data augmentation. Outsourcing GAN training to cloud servers can significantly reduce the computation load on local devices. Furthermore, in outsourcing settings, training data can be gathered ...Show More
Efficient Privacy-Preserving Multi-Functional Data Aggregation Scheme for Multi-Tier IoT System
Yunting Tao;Fanyu Kong;Yuliang Shi;Jia Yu;Hanlin Zhang;Huiyi Liu
2023 IEEE Symposium on Computers and Communications (ISCC)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
The proliferation of Internet of Things (IoT) devices has led to the generation of massive amounts of data that require efficient aggregation for analysis and decision-making. However, multi-tier IoT systems, which involve multiple layers of devices and gateways, face more complex security challenges in data aggregation compared to ordinary IoT systems. In this paper, we propose an efficient priva...Show More
Multi-hop Relational Graph Attention Network for Text-to-SQL Parsing
Hu Liu;Yuliang Shi;Jianlin Zhang;Xinjun Wang;Hui Li;Fanyu Kong
2023 International Joint Conference on Neural Networks (IJCNN)
Year: 2023 | Conference Paper |
Text-to-SQL aims to parse natural language problems into SQL queries, which can provide a simple interface to access large databases enabling SQL novices a quicker entry into databases. As the Text-to-SQL field is intensively studied, more and more models use GNNs to encode heterogeneous graph information in this task, and how to better obtain path information between nodes in database schema hete...Show More
The road network plays an important role in guiding people's daily travel. In the road network, the shortest distance query is one of the most basic query operations. With the scale of the road network continuously expanding, people choose to outsource the road network to the cloud server in the form of an encrypted graph. To the best of authors' knowledge, the existing privacy-preserving shortest...Show More
Privacy-Preserving Face Recognition With Multi-Edge Assistance for Intelligent Security Systems
IEEE Internet of Things Journal
Cited by: Papers (13)
Face recognition is one of the key technologies in intelligent security systems. Data privacy and identification efficiency have always been concerns about face recognition. Existing privacy-preserving protocols only focus on the training phase of face recognition. Since intelligent security systems mainly complete the calculation of large-scale face data in the identification phase, existing priv...Show More
Privacy-Preserving and Verifiable Outsourcing Message Transmission and Authentication Protocol in IoT
Hongjun Li;Fanyu Kong;Jia Yu;Hanlin Zhang;Luhong Diao;Yunting Tao
2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
With the popularity of Internet of Things (IoT) and 5G, privacy-preserving message transmission and authentication have become an indispensable part in the field of data collection and analysis. There exist many protocols based on the public key cryptosystem, which allow the users to utilize their own identity as the public key to carry out data encryption and digital signature, which is very suit...Show More
Secure Outsourcing for Normalized Cuts of Large-Scale Dense Graph in Internet of Things
IEEE Internet of Things Journal
Cited by: Papers (4)
With popularity and growth of cloud computing, outsourcing computation, as an important cloud service, has been applied in the field of academic and industry. It allows the resource-constrained IoT devices to outsource the computationally intensive problems to the cloud server. The smallest normalized cuts of the large-scale graph is a fundamental issue in graph theory, which is often used in vari...Show More
Secure Outsourcing of Large-Scale Convex Optimization Problem in Internet of Things
IEEE Internet of Things Journal
Cited by: Papers (7)
With the development of cloud computing and the advent of Internet of Things (IoT), outsourcing computation, as an important application of cloud computing, has been widely researched in the field of academic and industry. The convex optimization problem, as a most common mathematical problem, often appears in some machine learning algorithms and smart grid designs. However, the process of solving...Show More
Modification and Performance Improvement of Paillier Homomorphic Cryptosystem
2021 IEEE 19th International Conference on Embedded and Ubiquitous Computing (EUC)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Data security and privacy have become an important problem while big data systems are growing dramatically fast in various application fields. Paillier additive homomorphic cryptosystem is widely used in information security fields such as big data security, communication security, cloud computing security, and artificial intelligence security. However, how to improve its computational performance...Show More
Software Implementation and Comparison of ZUC-256, SNOW-V, and AES-256 on RISC-V Platform
2021 IEEE International Conference on Information Communication and Software Engineering (ICICSE)
Year: 2021 | Conference Paper |
Cited by: Papers (9)
As candidates for cryptographic primitives in 5G communication, ZUC-256, SNOW-V, and AES-256 offer the 256-bit security in terms of confidentiality and integrity. There have been some studies on software implementations on different platforms for them. In this paper, ZUC-256, SNOW-V, and AES-256 are implemented in the standard C language on RISC-V CPU without AES new instructions. Their implementa...Show More
Toward Verifiable Phrase Search Over Encrypted Cloud-Based IoT Data
IEEE Internet of Things Journal
Cited by: Papers (14)
Phrase search encryption, as an important technique in cloud-based IoT system, allows users to retrieve encrypted IoT data that contains a set of consecutive keywords. It plays an important role in cloud-based e-healthcare diagnosis system, machine learning applications for cloud-based IoT system, etc. However, to the best of our knowledge, the existing phrase search encryption schemes cannot achi...Show More
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