Article
Computer Science, Information Systems
M. A. P. Chamikara, P. Bertok, I. Khalil, D. Liu, S. Camtepe
Summary: Edge computing and distributed machine learning have advanced to a level that can revolutionize a particular organization. Federated learning (FedML) is a method to protect privacy in machine learning, but additional measures are needed to ensure data privacy.
COMPUTER COMMUNICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Paulo Silva, Carolina Goncalves, Nuno Antunes, Marilia Curado, Bogdan Walek
Summary: This study proposes a Personal Data Analyser, which enhances privacy assurances and minimizes privacy risks through automated privacy-preserving data monitoring and risk assessment mechanisms.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Sina Shaham, Ming Ding, Bo Liu, Shuping Dang, Zihuai Lin, Jun Li
Summary: Publishing datasets is crucial for open data research and government transparency, but can lead to privacy concerns, especially in spatiotemporal trajectory datasets. Applying privacy preserving techniques before publication is essential. This paper introduces a machine learning based framework for anonymizing datasets, improving utility while protecting privacy.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Shaoyong Guo, Fan Zhang, Song Guo, Siya Xu, Feng Qi
Summary: This article proposes a blockchain-assisted privacy-preserving distributed data computing architecture to overcome the limitations of existing research. The architecture ensures secure and trustworthy computing and incorporates a sandbox location obfuscation method based on onion routing technology, making it difficult for attackers to infer user privacy.
IEEE COMMUNICATIONS MAGAZINE
(2023)
Article
Computer Science, Information Systems
Changsong Jiang, Chunxiang Xu, Yuan Zhang
Summary: Privacy-preserving federated learning achieves privacy protection for user data through membership proof and the PFLM scheme, which eliminates the stringent requirements for thresholds in the original schemes. The new scheme utilizes cryptographic accumulators and a result verification algorithm based on ElGamal encryption to enhance security and correctness verification.
INFORMATION SCIENCES
(2021)
Review
Computer Science, Artificial Intelligence
Vankamamidi S. Naresh, Muthusamy Thamarai
Summary: Data mining and machine learning applications in medical diagnostic systems are growing, but data privacy is a major concern due to the sensitive nature of healthcare data. This article discusses the privacy and security challenges in these systems and explores privacy-preserving computation techniques for secure data evaluation and processing. The state-of-the-art applications and open challenges in healthcare are analyzed, including privacy-preserving data mining and machine learning, and federated learning.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2023)
Article
Engineering, Electrical & Electronic
Fahad Algarni, Mohammad Ayoub Khan, Wedad Alawad, Nadhir Ben Halima
Summary: Sensing devices, high-performance networking, and privacy preservation algorithms are crucial for protecting remotely sensed environmental data in smart cities. This article proposes a privacy-preserving scheme that effectively safeguards sensitive geosensed data from security threats. The scheme utilizes two-factor authentication and federated learning to ensure data privacy and authentication. By employing lightweight digital signing cryptography, the proposed scheme achieves higher authentication success rate, improved overlapping factor, and reduced authentication time, false data, and verification time.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Computer Science, Theory & Methods
Jianfei Sun, Guowen Xu, Tianwei Zhang, Xuehuan Yang, Mamoun Alazab, Robert H. Deng
Summary: The cloud-based data sharing technology with cryptographic primitives allows data owners to outsource data and share information privately with arbitrary recipients regardless of geographical barriers. However, existing efforts in outsourced data sharing are inefficient, inflexible, or insecure due to issues such as dynamic target ciphertext designation, identity hiding of recipients, and verification of outsourced ciphertext transformation. Motivated by this, we design VF-PPBA, the first Verifiable, Fair and Privacy-preserving Broadcast Authorization framework for flexible data sharing in clouds. Using a new primitive called privacy-preserving multi-recipient broadcast proxy re-encryption (PPMR-BPRE), we ensure efficient ciphertext transformation, identity protection, and verify the correctness of outsourced conversion tasks.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Artificial Intelligence
Hsin-Yi Chen, Szu-Hao Huang
Summary: Applying the privacy-preserving generative adversarial imitation network (PPGAIN) algorithm allows for establishing a profitable financial trading strategy while protecting the privacy of individuals. The generated trading behavior sequences are difficult to classify as imitations of specific individuals.
Article
Chemistry, Multidisciplinary
Andreea Bianca Popescu, Ioana Antonia Taca, Cosmin Ioan Nita, Anamaria Vizitiu, Robert Demeter, Constantin Suciu, Lucian Mihai Itu
Summary: The study introduces an encoding method to enable homomorphic encryption schemes to operate on real-valued numbers of arbitrary precision and size in EEG signal scenarios. The computational time for training models increases but remains manageable, while the inference time remains in the order of milliseconds. The prediction performance of models operating on encoded and encrypted data is comparable to standard models operating on plaintext data.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Multidisciplinary
Lihua Yin, Jiyuan Feng, Hao Xun, Zhe Sun, Xiaochun Cheng
Summary: The paper introduces a new hybrid privacy-preserving method for addressing data leakage threats in existing federated learning training processes. It utilizes advanced functional encryption algorithms and local Bayesian differential privacy to enhance data protection, while also implementing Sparse Differential Gradient to improve transmission and storage efficiency.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Biochemical Research Methods
Talal Ahmed, Mark A. Carty, Stephane Wenric, Jonathan R. Dry, Ameen A. Salahudeen, Aly A. Khan, Eric Lefkofsky, Martin C. Stumpe, Raphael Pelossof
Summary: Reproducibility of results obtained using RNA data in cancer research remains challenging. Current RNA correction algorithms require access to patient-level data, but SpinAdapt computes corrections using aggregate statistics, preserving patient data privacy. SpinAdapt outperforms other methods on publicly available cancer studies and can correct new samples for unbiased evaluation.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Health Care Sciences & Services
Tyler J. Loftus, Matthew M. Ruppert, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Jeremy A. Balch, Philip A. Efron, Gilbert R. Upchurch, Parisa Rashidi, Christopher Tignanelli, Jiang Bian, Azra Bihorac
Summary: In healthcare AI applications, maintaining generalizability, external validity, and reproducibility is crucial. Traditional approaches of sharing patient data can compromise data privacy and security. Federated learning techniques offer an alternative by sharing knowledge instead of data, preserving both data privacy and availability.
Article
Automation & Control Systems
Bin Zhao, Kai Fan, Kan Yang, Zilong Wang, Hui Li, Yintang Yang
Summary: This article presents a privacy-preserving federated learning scheme for mining industrial big data, exploring the impact of shared parameter proportions on accuracy through experiments. It is found that sharing partial parameters can almost achieve the same accuracy as sharing all parameters, reducing privacy leakage.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Theory & Methods
Sen Wang, J. Morris Chang
Summary: This paper proposes a distributed privacy-preserving boosting algorithm that leverages Local Differential Privacy as a building block to ensure the privacy of participated data owners. Experiments show that the algorithm effectively boosts various classifiers while maintaining high utility.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2021)
Article
Computer Science, Information Systems
Yong Yu, Yannan Li, Bo Yang, Willy Susilo, Guomin Yang, Jian Bai
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2020)
Article
Automation & Control Systems
Yong Yu, Yanqi Zhao, Yannan Li, Xiaojiang Du, Lianhai Wang, Mohsen Guizani
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Article
Computer Science, Information Systems
Ruonan Chen, Yannan Li, Yong Yu, Huilin Li, Xiaofeng Chen, Willy Susilo
IEEE INTERNET OF THINGS JOURNAL
(2020)
Article
Engineering, Electrical & Electronic
Yannan Li, Yong Yu, Ruonan Chen, Xiaojiang Du, Mohsen Guizani
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2020)
Article
Engineering, Electrical & Electronic
Yong Yu, Junbin Shi, Huilin Li, Yannan Li, Xiaojiang Du, Mohsen Guizani
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2020)
Article
Computer Science, Hardware & Architecture
Yannan Li, Guomin Yang, Willy Susilo, Yong Yu, Man Ho Au, Dongxi Liu
Summary: Monero offers high anonymity for users and transactions, but lacks user accountability, which is crucial to combat criminal activities in cryptocurrency transactions. This paper introduces Traceable Monero, a new cryptocurrency that aims to strike a balance between user anonymity and accountability. By overlaying Monero with tracing mechanisms, Traceable Monero ensures security without significantly impacting transaction efficiency.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Yannan Li, Yong Yu, Willy Susilo, Zhiyong Hong, Mohsen Guizani
Summary: 5G and beyond (B5G) networks are leading a digital revolution in both academia and industry, but still face challenges in terms of security and privacy. Blockchain, as a public database, with its advantages of decentralization, transparency, etc., is promising in solving security issues. This article investigates security and privacy issues in edge intelligence in B5G networks and proposes a framework integrating blockchain to provide guaranteed security and privacy.
IEEE WIRELESS COMMUNICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
Yanqi Zhao, Yong Yu, Ruonan Chen, Yannan Li, Aikui Tian
Summary: Machine learning is an effective approach for big data analysis, and classification is widely used in data processing. Balancing data utility and data privacy is a challenging issue in privacy-preserving data classification. The proposed transparent and accountable framework utilizes cryptography techniques and blockchain to balance data privacy and data utility.
Article
Computer Science, Hardware & Architecture
Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Dongxi Liu, Xiaojiang Du, Mohsen Guizani
Summary: The article introduces a framework of a self-tallying voting system in decentralized IoT based on blockchain, addressing the fairness issues inherent in self-tallying voting systems and proving the security of the system. The implementation results demonstrate the practicability of the system.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Review
Imaging Science & Photographic Technology
Yang-Wai Chow, Willy Susilo, Yannan Li, Nan Li, Chau Nguyen
Summary: The popularity of the Metaverse has grown rapidly in recent years. However, before it can be adopted for serious applications, there are cybersecurity issues related to visualization technologies that need to be addressed. This survey investigates the cybersecurity threats faced by the Metaverse and discusses countermeasures against them. Its intention is to provide researchers in related areas with a background understanding of the field.
JOURNAL OF IMAGING
(2023)
Article
Telecommunications
Yiting Huang, Yong Yu, Huilin Li, Yannan Li, Aikui Tian
Summary: The development and maturity of cloud computing technology have led to an increasing number of companies and individuals choosing to store their data in the cloud. However, issues such as cloud server downtime and data loss pose serious problems for applications that need to run continuously. Ensuring data privacy while ensuring data integrity is a pressing issue. Existing data integrity auditing schemes have security problems. To address these issues, a blockchain-based continuous data integrity checking protocol is proposed.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Proceedings Paper
Computer Science, Information Systems
Willy Susilo, Yannan Li, Fuchun Guo, Jianchang Lai, Ge Wu
Summary: Public cloud data auditing allows third parties to verify data integrity on untrusted servers without retrieving the data. Different schemes based on RSA and bilinear pairing have been proposed with trade-offs between proof size and storage cost. However, the balance between proof size and storage cost in cloud data auditing remains an open problem.
COMPUTER SECURITY - ESORICS 2022, PT II
(2022)
Proceedings Paper
Computer Science, Information Systems
Yannan Li, Willy Susilo, Guomin Yang, Tran Viet Xuan Phuong, Yong Yu, Dongxi Liu
Summary: Research on vector commitment and its variants has led to the proposal of a new primitive called mercurial subvector commitments, which exhibits efficient updateability, mercurial hiding, position binding, and aggregatability. The study formalizes system and security models for the primitive, presenting a concrete construction with security proofs demonstrating satisfaction of desired properties. Applications of mercurial subvector commitments, such as zero-knowledge sets and blockchain with account-based models, are also illustrated.
INFORMATION SECURITY AND PRIVACY, ACISP 2021
(2021)
Article
Engineering, Multidisciplinary
Huilin Li, Yannan Li, Yong Yu, Baocang Wang, Kefei Chen
Summary: Artificial intelligence (AI) has shown great potential in various real-world applications, but faces challenges in trust-oriented applications such as e-voting. This paper aims to strengthen AI ecosystems by developing a blockchain-based traceable self-tallying e-voting system, which supports additional functions like multi-choice and self-tallying, and satisfies anonymity, time-bounded privacy, linkability, and full-traceability. The proposed e-voting protocol is practical and can be adopted in real-world applications, as evaluated by the time and gas costs of operations.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Hardware & Architecture
Yannan Li, Yong Yu, Chunwei Lou, Nadra Guizani, Lianhai Wang
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
