Article
Computer Science, Hardware & Architecture
Zheli Liu, Siyi Lv, Jin Li, Yanyu Huang, Liang Guo, Yali Yuan, Changyu Dong
Summary: This article introduces the methods of order-preserving encryption (OPE) and order-revealing encryption (ORE), and proposes two new ORE schemes. These schemes achieve good results by reducing information leakage and maintaining practicality.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Information Systems
Yu Zhan, Danfeng Shen, Pu Duan, Benyu Zhang, Zhiyong Hong, Baocang Wang
Summary: This paper proposes a multi-dimensional data order preserving encryption scheme MDOPE, which allows fine-grained multi-dimensional range queries.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Jihye Yang, Kee Sung Kim
Summary: This research proposes a more efficient ciphertext update algorithm for mutable OPE schemes, reducing the number of updates by considering the input pattern of encrypted data without sacrificing security.
Article
Computer Science, Information Systems
Mohamed Alie Kamara, Xudong Li
Summary: Cloud computing services are gaining rapid attention from organizations due to cost-effectiveness, but face security challenges in protecting client data. Order-preserving encryption and cloud storage encryption are important techniques for database security, but commonly leak the distribution of repeated plaintext values. This paper introduces a random perturbation distribution scheme (RPDS) to securely handle repeated plaintext values without leaking their distribution.
Article
Computer Science, Information Systems
Shijun Xiang, Guanqi Ruan, Hao Li, Jiayong He
Summary: Database security has always been a hot topic in the field of information security. This study proposes a robust watermarking scheme for the protection of databases in the encrypted domain by combining encryption and digital watermarking technology.
FRONTIERS OF COMPUTER SCIENCE
(2022)
Article
Computer Science, Information Systems
Yushu Zhang, Wentao Zhou, Ruoyu Zhao, Xinpeng Zhang, Xiaochun Cao
Summary: This study proposes a new encryption scheme that achieves a balance between privacy protection and usability by improving the connectivity issue of Markov chains in existing schemes. The proposed scheme effectively encrypts images and protects privacy, while also being more efficient than existing methods.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Interdisciplinary Applications
Sana Keita, Abdelaziz Beljadid, Yves Bourgault
Summary: This study introduces a new finite element approximation method for fourth-order parabolic equations, along with a numerical convergence study and tests demonstrating the effectiveness and robustness of the method. The results indicate that the proposed method can satisfy the desired physical properties of the solution and converge to the truncation schemes.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Computer Science, Information Systems
Si Chen, Lin Li, Wenyu Zhang, Xiaolin Chang, Zhen Han
Summary: Researchers are increasingly interested in directly performing operations on encrypted databases using the Order-Preserving Encryption (OPE) scheme. This paper introduces a new scheme, BOPE, which achieves high performance and ideal security through a searching algorithm and an updating algorithm. Experimental results show that BOPE outperforms traditional scheme mOPE.
Article
Computer Science, Information Systems
Xinle Cao, Jian Liu, Yongsheng Shen, Xiaohua Ye, Kui Ren
Summary: This paper investigates the security of frequency-hiding order-preserving encryption (FH-OPE) schemes. By presenting three ciphertext-only attacks, we demonstrate that the hidden plaintext frequency in existing FH-OPE schemes can be recovered. These findings highlight the limitations of current FH-OPE schemes.
PROCEEDINGS OF THE VLDB ENDOWMENT
(2023)
Article
Computer Science, Information Systems
Dongjie Li, Siyi Lv, Yanyu Huang, Yijing Liu, Tong Li, Zheli Liu, Liang Guo
Summary: This article proposes a new frequency-hiding order-preserving encryption (FH-OPE) scheme that achieves small client storage without additional client-server interactions. The scheme achieves small client storage and 1 interaction per query, making it more efficient than previous FH-OPE schemes.
PROCEEDINGS OF THE VLDB ENDOWMENT
(2021)
Article
Computer Science, Theory & Methods
Xue-ping Wang, Cheng-lin Zhu
Summary: This article investigates the new construction of semi-t-operators on bounded lattices. It gives five methods to construct semi-t-operators on bounded lattices using semi-t-norms, semi-t-conorms, and order-preserving mappings. The relationships between these methods and well-known ones are also discussed, revealing that known methods in literature are special cases of these ones.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Information Systems
Wenju Xu, Baocang Wang, Jiasen Liu, Yange Chen, Pu Duan, Zhiyong Hong
Summary: In this paper, the system model for training linear regression models over rational numbers using linearly homomorphic encryption is improved. Each data owner generates their own public key and secret key, and an improved multi-key fully homomorphic encryption method is utilized. The proposed algorithm is shown to be more feasible and practical through performance analyses.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Payal Chaudhari, Manik Lal Das
Summary: Searchable encryption allows cloud servers to search encrypted data without decryption. Single keyword-based encryption enables users to access subsets of documents containing specific keywords. The scheme presented in this paper uses attribute-based encryption to grant access to selective data subsets while maintaining user privacy.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2021)
Article
Automation & Control Systems
Wentuo Fang, Mohsen Zamani, Zhiyong Chen
Summary: This paper focuses on secure and privacy-preserving consensus algorithms for networked systems. It explores an average consensus algorithm for systems with second-order dynamics using Paillier encryption and adds randomness to network weights. The study thoroughly examines the conditions for privacy preservation, especially in relation to consensus rate, through theoretical analysis and numerical verification.
SYSTEMS & CONTROL LETTERS
(2021)
Article
Computer Science, Information Systems
Tham Nguyen, Naveen Karunanayake, Sicong Wang, Suranga Seneviratne, Peizhao Hu
Summary: Conventional spam classification requires revealing email contents for text analysis, while new cryptographic primitives allow encrypted email classification without compromising user data privacy. In this paper, a spam classification framework based on a neural network is proposed. By utilizing homomorphic encryption and functional encryption, the framework achieves high accuracy in predicting encrypted email labels. Performance study and security analysis reveal trade-offs between the two encryption methods.
COMPUTER COMMUNICATIONS
(2023)
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)