Article
Computer Science, Information Systems
Hassan Mahdikhani, Rongxing Lu, Jun Shao, Ali Ghorbani
Summary: The article proposes a new efficient and privacy-preserving range query scheme in fog-based IoT, utilizing decomposition technique and symmetric homomorphic encryption for privacy protection and improved query efficiency. Security analysis shows the scheme is privacy preserving, and performance evaluations demonstrate its higher efficiency compared to previous schemes in terms of computational overhead and communication complexity.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Kun-Chang Li, Run-Hua Shi
Summary: In this article, a new consortium blockchain-enhanced IoT architecture is creatively designed, and a novel consortium blockchain-based privacy-preserving range query scheme is proposed. The scheme combines the consortium blockchain and inner product function encryption technology to achieve flexible, safe, and batch range query. Security analysis and performance evaluation demonstrate the effectiveness, scalability, and flexibility of the proposed scheme.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Hardware & Architecture
Yu Guo, Hongcheng Xie, Cong Wang, Xiaohua Jia
Summary: This article presents a geographic range-match scheme for fog-enhanced services that securely collects sensed data while protecting the location privacy of IoT devices. By formulating the problem as range-based pattern matching and designing security schemes in the ciphertext domain, efficient range queries can be performed with reduced accessible information.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Information Systems
Savio Sciancalepore, Roberto Di Pietro
Summary: The proposed protocol PPRQ enables privacy-preserving range queries in IoT networks through a probabilistic scheme, achieving over 99.9% query result accuracy, robustness, and resilience against adversaries in various scenarios. The protocol requires only hashing and bitwise xor operations, making it lightweight and scalable for different types of range queries.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Min Zhou, Yandong Zheng, Yunguo Guan, Limin Peng, Rongxing Lu
Summary: Smart agriculture IoT utilizes IoT technology for automatic agricultural management, but agricultural IoT devices have limited resources. To protect data privacy, fog nodes are placed at the edge to process agriculture data, but there are risks of privacy leakage. The research achieved privacy-preserving range-max queries in fog-based smart agriculture IoT through encryption and comparison techniques.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Shuai Shang, Xiong Li, Rongxing Lu, Jianwei Niu, Xiaosong Zhang, Mohsen Guizani
Summary: This article proposes a privacy-preserving multidimensional range query scheme, called Edge-PPMRQ, for edge-supported Industrial Internet of Things (IIoT). By designing a novel range division algorithm, efficient multidimensional range query is achieved, and detailed security analysis and performance evaluation are conducted.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Theory & Methods
Kinza Sarwar, Sira Yongchareon, Jian Yu, Saeed Ur Rehman
Summary: Despite the challenges in adopting IoT due to data privacy concerns, the introduction of fog computing can address some of the issues and provide improvements for preserving data privacy in IoT applications. Future research directions in this area are also discussed.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Yinglong Li, Weiru Liu, Yihua Zhu, Hong Chen, Hongbing Cheng, Tieming Chen, Ping Hu, Ruohong Huan
Summary: This article proposes two privacy-aware fuzzy query processing schemes based on fuzzy theory and introduces linguistic range variables, fuzzy overlap information, and its recovery mechanism. It also devises two distributed privacy-aware fuzzy range query processing algorithms. The approaches aim to provide optimal performances in terms of privacy protection, reliability, energy efficiency, and real-time response.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Dandan Li, Xiaohong Huang, Wei Huang, Fei Gao, Shen Yan
Summary: The development of the Internet of Things has brought various convenient IoT services, but privacy leakage is a serious issue. The privacy-preserving problem of IoT query has attracted much attention, especially on how to design secure schemes under the threat of quantum computation.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Engineering, Multidisciplinary
Hanan Naser Alsuqaih, Walaa Hamdan, Haythem Elmessiry, Hussein Abulkasim
Summary: The development of IoT has enabled remote health data analysis, but protecting patients' data privacy is challenging. Blockchain technology is proposed as a solution to enable secure and private exchange of personal health data. This work addresses the inadequacy of previous work in providing safe and privacy-preserving diagnostic enhancement for e-Health platforms.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Information Systems
Jasleen Kaur, Alka Agrawal, Raees Ahmad Khan
Summary: With the growth of the digital population, managing users' private data flowing across the web has become challenging. Fog computing has addressed certain issues but also raised concerns about privacy. The authors propose an encryfuscation model that employs obfuscation and encryption techniques, selecting suitable privacy preservation techniques based on offloading decisions. They also propose obfuscation techniques for data and location.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Zhengxin Yu, Jia Hu, Geyong Min, Zi Wang, Wang Miao, Shancang Li
Summary: Fog Radio Access Networks (F-RANs) are a promising paradigm to support the increasing demands of multimedia services. To address privacy concerns, a federated learning-based cooperative hierarchical caching scheme (FLCH) is proposed, utilizing local data and IoT devices to train a shared learning model for content popularity prediction.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Automation & Control Systems
Qinglei Kong, Feng Yin, Rongxing Lu, Beibei Li, Xiaohong Wang, Shuguang Cui, Ping Zhang
Summary: This article introduces a federated learning-based automotive navigation framework called FedLoc, which addresses the issues of weak GPS signals, user privacy, and flexibility with an efficient, flexible, and privacy-preserving model aggregation scheme. By utilizing technologies such as homomorphic threshold cryptosystem and bounded Laplace mechanism, the locally trained model updates are protected and malicious users are deterred effectively.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Shelendra Kumar Jain, Nishtha Kesswani
Summary: This article proposes a noise-based privacy-preserving model that effectively protects sensitive data through mechanisms such as multilevel noise treatment, user preferences-based data classifier, and noise removal. Experimental results demonstrate that the proposed model outperforms existing approaches in terms of computational overhead.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Information Systems
Mehedi Masud, Gurjot Singh Gaba, Pardeep Kumar, Andrei Gurtov
Summary: Ambient Intelligence (AmI) in Internet of Things (IoT) has enabled remote monitoring, diagnosis, and treatment in healthcare, improving patient engagement and satisfaction. However, current healthcare applications face security and resource efficiency challenges. This paper presents a computationally-inexpensive privacy-assuring authentication protocol for AmI-IoT healthcare applications, utilizing blockchain and fog computing to ensure security and efficiency.
COMPUTER COMMUNICATIONS
(2022)
Article
Computer Science, Theory & Methods
Yandong Zheng, Rongxing Lu, Yunguo Guan, Songnian Zhang, Jun Shao, Hui Zhu
Summary: This paper proposes an efficient access pattern privacy-preserving similarity range query scheme with access control in the context of eHealthcare, where healthcare data are outsourced in an encrypted form and need to be accessed in a privacy-preserving way. The proposed scheme utilizes a novel tree structure and symmetric homomorphic encryption to achieve privacy protection and efficient querying of healthcare data.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Theory & Methods
Yandong Zheng, Rongxing Lu, Hui Zhu, Songnian Zhang, Yunguo Guan, Jun Shao, Fengwei Wang, Hui Li
Summary: The advance of cloud computing has led to outsourcing large-scale data and data-driven services to public clouds. However, there is a lack of research on privacy-preserving set reverse k nearest neighbors (RkNN) query. In this paper, an efficient and privacy-preserving scheme for set RkNN query over encrypted data is proposed, with sublinear query efficiency.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Information Systems
Yang Ming, Pengfei Yang, Hassan Mahdikhani, Rongxing Lu
Summary: The Industrial Internet of Things (IIoT) is changing production methods by allowing users to access data directly from smart devices through the network. However, the transmission of data from these devices is usually insecure, leading to security concerns. This article proposes a secure one-to-many authentication and key agreement scheme for IIoT, using smart cards, passwords, and biometrics to authenticate users. The scheme utilizes elliptic curve cryptography and the Chinese remainder theorem to agree upon different session keys between a user and multiple smart devices in a single request.
IEEE SYSTEMS JOURNAL
(2023)
Article
Computer Science, Hardware & Architecture
Songnian Zhang, Suprio Ray, Rongxing Lu, Yandong Zheng, Yunguo Guan, Jun Shao
Summary: Skyline queries are promising and practical techniques in multi-criteria decision making applications. However, privacy concerns have led to the need for privacy-preserving skyline queries over encrypted data. Existing solutions lack support for user-defined query criteria and require multi-round communications between two servers. In this article, we propose a privacy-preserving user-defined skyline query scheme in a single-server model, eliminating the need for extra communications. The scheme is shown to be selectively secure and outperforms alternative schemes in terms of computational costs.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Songnian Zhang, Suprio Ray, Rongxing Lu, Yandong Zheng, Yunguo Guan, Jun Shao
Summary: This paper proposes an efficient and privacy-preserving interval skyline query scheme using symmetric homomorphic encryption. The scheme includes secure protocols for sorting the encrypted dataset and determining dominance relations of time series data, as well as a lookup table for quick query response. Security analysis demonstrates that the proposed scheme protects outsourced data, query results, and single-dimensional privacy while hiding access patterns. Evaluation results show that the proposed scheme outperforms existing solutions in computational and communication costs by orders of magnitude.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Information Systems
Songnian Zhang, Suprio Ray, Rongxing Lu, Yandong Zheng
Summary: Researchers have recently demonstrated that learned indexes can improve query performance and reduce storage overhead, offering an opportunity to address the challenges of spatial query processing caused by location-based services. However, existing approaches to process spatial data using learned indexes do not fully leverage the spatial distribution information of the original data. In this paper, the limitations of a previous spatial learned model are addressed with a new model that incorporates the characteristics of spatial interpolation functions and a dynamic encoding technique.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Artificial Intelligence
Xingchen Zhang, Yiannis Demiris
Summary: Visible and infrared image fusion (VIF) has gained considerable attention for its applications in various tasks, and there has been an increasing number of deep learning-based VIF methods proposed in recent years. This paper presents a comprehensive review of these methods, discussing motivation, taxonomy, recent developments, datasets, evaluation methods, and future prospects in detail. It serves as a valuable reference for VIF researchers and those interested in this rapidly developing field.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Yandong Zheng, Rongxing Lu, Yunguo Guan, Songnian Zhang, Jun Shao, Fengwei Wang, Hui Zhu
Summary: In this work, an efficient and privacy-preserving spatial-feature-based RkNN scheme is proposed. The scheme supports spatial data with many features and utilizes a modified intersection and union R tree (MIUR-tree) for indexing. A private filter protocol and a private refinement protocol are designed based on a symmetric homomorphic encryption (SHE) scheme, enabling the privacy-preserving RkNN query.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Samaneh Mahdavifar, Ali A. A. Ghorbani
Summary: In this article, a rule extraction method called CapsRule is proposed for classifying network attacks. It extracts high-fidelity rules from the feed-forward capsule network that explain how an input sample is classified. The evaluation shows that CapsRule generates accurate, high-fidelity, and comprehensible rules, and helps identify errors and noise in the data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Yandong Zheng, Hui Zhu, Rongxing Lu, Yunguo Guan, Songnian Zhang, Fengwei Wang, Jun Shao, Hui Li
Summary: The prevalence of cloud computing has led to the popularity of outsourced query services. Graph similarity query, which measures the similarity between two graphs using graph edit distance (GED), is an important type of query. Existing schemes for GED computation/graph similarity query do not consider data privacy and are not suitable for cloud computing. In this paper, we propose a privacy-preserving graph similarity query scheme called PGSim, which applies a filter and verification framework. Our scheme efficiently retrieves candidate graphs using a pivot R-tree based filter algorithm and verifies them using a GED query verification algorithm. To ensure privacy, we employ symmetric homomorphic encryption and design a PRFilter scheme along with a PGQVerify algorithm. Security analysis shows that our scheme is selectively secure and performance evaluation demonstrates its high efficiency.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Cybernetics
Xichen Zhang, Sajjad Dadkhah, Alexander Gerald Weismann, Mohammad Amin Kanaani, Ali A. Ghorbani
Summary: With the rapid development of computer vision techniques, multimodal analyses are widely used in online fake news detection. This article proposes and evaluates four image-text similarities to understand the role of image-text relationship in fake news detection. The findings show that fake news image-text similarity is higher than real news image-text similarity in most cases, supporting the significance of visual information in fake news detection.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yaxi Yang, Yao Tong, Jian Weng, Yufeng Yi, Yandong Zheng, Leo Yu Zhang, Rongxing Lu
Summary: This article presents a query scheme for privacy-preserving on genomic data, which allows secure querying of specific ranges of genomic data in a database.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Computer Science, Information Systems
Miraqa Safi, Sajjad Dadkhah, Farzaneh Shoeleh, Hassan Mahdikhani, Heather Molyneaux, Ali A. Ghorbani
Summary: This article provides a comprehensive review of various IoT device profiling methods and offers a clear taxonomy for IoT profiling techniques based on different security perspectives. It investigates current IoT device profiling techniques and applications, analyzes device vulnerabilities, and discusses machine learning and deep learning algorithms used for IoT device profiling. The article concludes with a discussion of challenges and future research possibilities in this domain.
ACM TRANSACTIONS ON INTERNET OF THINGS
(2022)
Article
Computer Science, Theory & Methods
Songnian Zhang, Suprio Ray, Rongxing Lu, Yunguo Guan, Yandong Zheng, Jun Shao
Summary: This paper proposes a privacy-preserving aggregate reverse skyline query (PPARS) scheme on a single server model while ensuring full query privacy. The scheme transforms the problem of ARS query into a combination of set membership test and logical expressions, and utilizes encryption, encoding, and homomorphic encryption techniques to obtain encrypted aggregate values without leaking query privacy. The communication efficiency is improved by an interpolation-based packing technique.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Information Systems
Yandong Zheng, Rongxing Lu, Jun Shao, Fan Yin, Hui Zhu
Summary: Dynamic symmetric searchable encryption (SSE) enables secure search and dynamic update of encrypted documents in a semi-trusted cloud server. Existing SSE schemes have privacy leaks, and none of them can preserve search pattern privacy or enhance backward privacy. In this article, a practical SSE scheme is proposed, which supports search pattern privacy and enhances backward privacy through an obfuscating technique, pseudorandom function, and pseudorandom generator. Security analysis and performance evaluations demonstrate the effectiveness and efficiency of the proposed scheme.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)