Article
Computer Science, Information Systems
Xiaoguo Li, Tao Xiang, Shangwei Guo, Hongwei Li, Yi Mu
Summary: This article investigates privacy-preserving reverse nearest neighbor (PPRNN) query over encrypted spatial data. It introduces a new PPRNN scheme sPPRNN and its extension to the dynamic setting dPPRNN, and conducts a thorough privacy analysis, demonstrating the efficiency and effectiveness of the proposal through extensive experiments.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Jin Li, Heng Ye, Tong Li, Wei Wang, Wenjing Lou, Y. Thomas Hou, Jiqiang Liu, Rongxing Lu
Summary: This article discusses the application of differential privacy in data privacy protection and proposes two schemes for outsourcing differential privacy. These schemes effectively address the issues of current differential privacy techniques in adapting to different tasks and budgets, and their effectiveness is verified through experiments.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Hassan Nasiraee, Maede Ashouri-Talouki
Summary: This paper introduces the Edge-Fog-Cloud interplay in the Internet-of-Things (IoT) and proposes a new Privacy-preserving Distributed data Access control (PDAC) system. The PDAC system improves the previous distributed ABE systems by introducing user's anonymity approach, novel policy-hiding mechanism, and independent-authorities system, while enhancing efficiency through offloading user's computations to the Cloud servers.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Information Systems
Keke Gai, Meikang Qiu, Hui Zhao
Summary: With the rapid growth of big data applications in cloud computing, privacy has become a significant concern. While the implementation of emerging technologies has brought many benefits and improved application performance, challenges such as data encryption execution time have also emerged. Many current applications prioritize performance over privacy by abandoning data encryption.
IEEE TRANSACTIONS ON BIG DATA
(2021)
Article
Computer Science, Information Systems
Jun Zhang, Zoe L. Jiang, Ping Li, Siu Ming Yiu
Summary: Preparing large amounts of training data is crucial for the success of machine learning, while privacy-preserving techniques like homomorphic encryption are proposed to address individual privacy concerns. Collaboration between different institutions is common in the era of big data, but there are risks to data privacy when encrypting data under a single key in multi-institution scenarios.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Kexin Xu, Benjamin Hong Meng Tan, Li-Ping Wang, Khin Mi Mi Aung, Huaxiong Wang
Summary: This paper focuses on maintaining privacy during decision tree evaluation by using homomorphic encryption, providing data confidentiality against semi-honest adversaries. The proposed construction allows both the client and the model holder to be offline during evaluation, achieving single-branch evaluation with significantly less computation compared to previous schemes. A proof-of-concept implementation demonstrates the effectiveness, and multi-key encryption and joint decryption support multi-client scenarios.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Jianfei Sun, Guowen Xu, Tianwei Zhang, Hu Xiong, Hongwei Li, Robert H. Deng
Summary: Benefiting from the powerful computing and storage capabilities of cloud services, data sharing in the cloud has been widely used in various applications. However, concerns about data privacy breaches have arisen due to outsourcing data to untrusted cloud. To address this issue, this article proposes an Efficient, Scalable and Privacy-preserving Data sharing framework over encrypted cloud dataset (ESPD). Unlike previous works, ESPD supports sharing target data to multiple users with distinct secret keys and maintains a constant ciphertext length. Security analysis and real-world experiments demonstrate the desirable performance of ESPD compared to other similar schemes.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Computer Science, Information Systems
Kun Yang, Chengliang Tian, Hequn Xian, Weizhong Tian, Yan Zhang
Summary: This paper introduces encryption methods for privacy protection in cloud databases and improves the security and efficiency through an improved algorithm.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Somayeh Sobati Moghadam, Amjad Fayoumi, Peyman Vafadoost
Summary: Pavan is a practical system that securely stores data in a cloud database while still enabling encrypted data to be processed. It uses an order-preserving partially homomorphic encryption scheme for query processing, allowing data privacy preservation with reasonable overhead and enabling data processing and sharing with multiple users when outsourced on the cloud.
Article
Computer Science, Hardware & Architecture
Shlomi Dolev, Peeyush Gupta, Yin Li, Sharad Mehrotra, Shantanu Sharma
Summary: The paper introduces algorithms for data outsourcing based on Shamir's secret-sharing scheme and for executing privacy-preserving SQL queries using MapReduce. These algorithms prevent an adversary from knowing the database or the query, and also prevent output-size and access-pattern attacks.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2021)
Article
Computer Science, Information Systems
Na Wang, Shancheng Zhang, Zheng Zhang, Junsong Fu, Jianwei Liu, Ruijin Wang
Summary: The Internet of Medical Things (IoMT) is an important application of the Internet of Things in health care. In this study, the authors propose a new Efficient Encrypted Parallel Ranking (EEPR) search system for encrypted cloud healthcare data, addressing the issues of inefficient retrieval and increased privacy risk faced by existing schemes. The proposed system demonstrates better search performance and enhanced privacy protection, and it is resistant to known background attacks, showing significantly improved time complexity and search efficiency compared to existing schemes.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Information Systems
Hao Ren, Hongwei Li, Dongxiao Liu, Guowen Xu, Nan Cheng, Xuemin Shen
Summary: In this paper, an efficient verifiable deep packet inspection (EV-DPI) scheme is proposed to address privacy concerns in outsourced middlebox services. The scheme utilizes a two-layer architecture with non-collusion cloud servers, preserving packet privacy and confidentiality of inspection rules. Experimental results on the Amazon Cloud demonstrate the high efficiency and strong control of the proposed EV-DPI scheme.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Computer Science, Information Systems
Haoyang Wang, Kai Fan, Kuan Zhang, Zilong Wang, Hui Li, Yintang Yang
Summary: Research on mobile cyber-physical systems is a growing trend, with applications in fields such as intelligent transportation systems and smart home appliances. However, the sharing and security of the massive amount of data collected by sensors remains a challenge.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Hardware & Architecture
Yandong Zheng, Rongxing Lu, Yunguo Guan, Jun Shao, Hui Zhu
Summary: This article proposes a practical and privacy-preserving multi-dimensional range query (PRQ) scheme, which utilizes R-tree and lightweight matrix encryption technique to address the issues of single-dimensional privacy leakage, inefficiency, and the requirement of two cloud servers in existing solutions.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Engineering, Electrical & Electronic
Yushu Zhang, Ruoyu Zhao, Xiangli Xiao, Rushi Lan, Zhe Liu, Xinpeng Zhang
Summary: With the popularity of cloud storage services, the privacy and user experience issues of storing sensitive images in the cloud have gained attention. Researchers propose a new high-fidelity thumbnail-preserving encryption scheme (HF-TPE) to address the shortcomings of existing schemes.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Ziqiang Yu, Fatos Xhafa, Yuehui Chen, Kun Ma
Article
Computer Science, Artificial Intelligence
Jindan Zhang, Baocang Wang, Fatos Xhafa, Xu An Wang, Cong Li
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2019)
Article
Computer Science, Information Systems
Fatos Xhafa, Andrew W. H. Ip
ENTERPRISE INFORMATION SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Vikash Kumar Singh, Sajal Mukhopadhyay, Fatos Xhafa, Aniruddh Sharma
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2020)
Article
Computer Science, Information Systems
Vikash Kumar Singh, Sajal Mukhopadhyay, Fatos Xhafa
ENTERPRISE INFORMATION SYSTEMS
(2020)
Article
Computer Science, Theory & Methods
Xu An Wang, Fatos Xhafa, Jianfeng Ma, Zhiheng Zheng
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2019)
Article
Computer Science, Hardware & Architecture
J. Beneicke, A. A. Juan, F. Xhafa, D. Lopez-Lopez, A. Freixes
IEEE CONSUMER ELECTRONICS MAGAZINE
(2020)
Article
Computer Science, Theory & Methods
Fatos Xhafa, Burak Kilic, Paul Krause
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Computer Science, Theory & Methods
Fatos Xhafa, Alhassan Aly, Angel A. Juan
Summary: The fast development of IoT and Cloud technologies has led to the emergence of new computing paradigms like Fog and Edge computing, providing new opportunities for novel application scenarios. However, this also brings new computing challenges, such as allocating applications to different computing nodes.
Article
Chemistry, Analytical
Anjan Bandyopadhyay, Vikash Kumar Singh, Sajal Mukhopadhyay, Ujjwal Rai, Fatos Xhafa, Paul Krause
Editorial Material
Computer Science, Information Systems
Fatos Xhafa
ENTERPRISE INFORMATION SYSTEMS
(2021)
Article
Chemistry, Analytical
Arijit Nandi, Fatos Xhafa, Laia Subirats, Santi Fort
Summary: Emotions and emotional intelligence play crucial roles in both face-to-face and online learning, influencing learning outcomes. While existing approaches are suitable for offline emotion classification, they are not suitable for real-time classification. Therefore, a proposed online-trained real-time emotion classification system offers a solution.
Article
Chemistry, Multidisciplinary
Surja Sanyal, Vikash Kumar Singh, Fatos Xhafa, Banhi Sanyal, Sajal Mukhopadhyay
Summary: This paper explores a framework for surplus food redistribution to address global food insecurity and wastage issues. Utilizing information and communications technology (ICT)-mediated food redistribution is a highly scalable approach that can facilitate better exchange of surplus food.
APPLIED SCIENCES-BASEL
(2021)
Article
Automation & Control Systems
Flora Amato, Giovanni Cozzolino, Francesco Moscato, Vincenzo Moscato, Fatos Xhafa
Summary: The interest in smart contracts and blockchain in Industry 4.0 is increasing. Smart contracts enable automation of processes but must comply with laws. This article proposes a model to validate the legal compliance of smart contracts in IoT environments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Proceedings Paper
Computer Science, Software Engineering
Fatos Xhafa, David Zaragoza, Santi Caballe
INNOVATIVE MOBILE AND INTERNET SERVICES IN UBIQUITOUS COMPUTING, IMIS-2018
(2019)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)