Article
Computer Science, Information Systems
Chang Xu, Jiachen Wang, Liehuang Zhu, Kashif Sharif, Chuan Zhang, Can Zhang
Summary: Medical service recommendation is essential in eHealthcare systems, but ensuring privacy while recommending doctors is challenging. This paper proposes a privacy-preserving multi-level attribute based scheme to provide personalized medical service recommendation. Two algorithms are designed to keep user demands secret and recommend doctors in a privacy-preserving way, with detailed analysis proving its security prosperities and performance evaluations demonstrating its efficiency.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Shenqing Wang, Chunpeng Ge, Lu Zhou, Huaqun Wang, Zhe Liu, Jian Wang
Summary: The use of Internet of Things (IoT) in eHealthcare field, especially in predicting patients' health status based on their daily activity data, has gained attention and research. However, using classification models for predictions requires significant computing resources that healthcare centers may not afford. The use of cloud computing can address the resource issue, but it raises concerns about user privacy leakage. Therefore, a method is proposed to store patients' data and classification models separately in multiple clouds, ensuring privacy protection and reducing the computational cost of healthcare centers.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Chang Xu, Zijian Chan, Liehuang Zhu, Rongxing Lu, Yunguo Guan, Kashif Sharif
Summary: The advancements and adoption of cloud-assisted ehealthcare systems enable efficient and easy access to massive electronic medical records (EMRs) stored in the cloud. Patients can search for similar EMRs as references, which helps them find appropriate medical services quickly. However, ensuring the efficiency and privacy of queries remains a challenge in large-scale ehealthcare systems. This study proposes an efficient and privacy-preserving similar EMR query scheme to address this challenge and help patients find similar EMRs in a large-scale ehealthcare system.
COMPUTER STANDARDS & INTERFACES
(2024)
Article
Engineering, Electrical & Electronic
Zengpeng Li, Mamoun Alazab, Sahil Garg, M. Shamim Hossain
Summary: Insufficient parking space and traffic congestion are common issues in urban life, and managing parking spaces requires privacy protection. Traditional centralized parking management methods have single points of failure and are not suitable for large organizations. Blockchain technology can provide decentralized solutions, but most current parking recommendation solutions overlook the issue of protecting driver privacy.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Telecommunications
Yucheng Chen, Chenyuan Feng, Daquan Feng
Summary: With growing concern on data privacy, implementing RS in a federated learning (FL) manner is proposed as an efficient approach to tackle the risk of privacy disclosure. However, most related works ignore the communication efficiency and are not suitable for large-scale networks. To address these issues, we propose a privacy-preserving hierarchical federated collaborative filtering scheme for the RS, which maintains good recommendation accuracy, preserves data privacy, and reduces communication overhead.
IEEE COMMUNICATIONS LETTERS
(2023)
Article
Computer Science, Information Systems
Cong Peng, Debiao He, Jianhua Chen, Neeraj Kumar, Muhammad Khurram Khan
Summary: The eHealth system is important in healthcare services, and the EPRT scheme proposed in this article aims to protect privacy and ensure secure computing, with performance comparison demonstrating its effectiveness and accuracy.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2021)
Article
Chemistry, Multidisciplinary
Bin Cheng, Ping Chen, Xin Zhang, Keyu Fang, Xiaoli Qin, Wei Liu
Summary: With the rapid development of ubiquitous data collection and analysis, data privacy in recommended systems is facing challenges. Differential privacy technology can protect privacy but introduces unwanted noise. Considering personalized requirements, a collaborative filtering algorithm is proposed to reduce unwanted noise and protect privacy. Experimental results show improved recommendation performance and privacy protection.
APPLIED SCIENCES-BASEL
(2023)
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, Hardware & Architecture
Chang Xu, Zijian Chan, Liehuang Zhu, Can Zhang, Rongxing Lu, Yunguo Guan
Summary: This study proposes two efficient and privacy-preserving electronic medical records query schemes with forward privacy in a multiuser setting (EPPFM). By using searchable encryption, the scheme can provide patients with historical electronic medical records that are consistent with their symptom keywords and have high service scores for reference. Through detailed security analysis and simulations, the study demonstrates the security and efficiency of the proposed schemes.
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
(2023)
Article
Computer Science, Information Systems
Yandong Zheng, Rongxing Lu, Songnian Zhang, Yunguo Guan, Jun Shao, Fengwei Wang, Hui Zhu
Summary: This article addresses the privacy concerns in healthcare data outsourcing by proposing an efficient and privacy-preserving multidimensional range query scheme. By building an R-tree to index the dataset and designing data comparison algorithms and a homomorphic encoding technique, the scheme aims to protect the privacy of encrypted data while ensuring computational efficiency.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Mingwu Zhang, Yu Chen, Jingqiang Lin
Summary: The outsourcing of patients' physiological data and medical records to the medical cloud provides valuable services for diagnosis and treatment, but also raises privacy concerns. This article proposes a privacy-preserving optimization of neighborhood-based recommendation scheme to securely recommend medical-aided diagnosis and treatment without revealing sensitive information. The scheme utilizes encryption, graph theory, BLS signature, and oblivious transfer protocol to ensure security and confidentiality, with efficient performance in computational costs and communication overheads.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Yandong Zheng, Rongxing Lu, Yunguo Guan, Jun Shao, Hui Zhu
Summary: In this article, a new efficient privacy-preserving similarity range query scheme (EPSim) is proposed. It addresses the limitations of existing methods in terms of security, efficiency, and practicality. By utilizing a modified encryption scheme and a Quadsector tree, an efficient algorithm for similarity range queries is designed. Security analysis confirms the proposed EPSim scheme's robustness, and performance evaluations demonstrate its efficiency and practicality.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Information Systems
Jiangang Shu, Xiaohua Jia, Kan Yang, Hua Wang
Summary: This paper proposes a privacy-preserving task recommendation scheme (PPTR) for crowdsourcing, which protects both task privacy and worker privacy by utilizing polynomial functions and matrix decomposition for multi-keyword matching. User accountability and revocation are effectively achieved through PPTR. Extensive privacy analysis and performance evaluations demonstrate that PPTR is secure and efficient.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Chemistry, Multidisciplinary
Muhammad Asad, Saima Shaukat, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada
Summary: Big data is a rapidly growing field, and one of the new developments in this area is the use of federated learning for recommendation systems. This approach allows for user privacy protection by training recommendation models using intermediate parameters instead of real user data. This paper explores the current research on federated learning recommendation systems, addressing existing issues, potential applications, and challenges associated with their development and deployment.
APPLIED SCIENCES-BASEL
(2023)
Article
Telecommunications
Dengcheng Yan, Yuchuan Zhao, Zhongxiu Yang, Ying Jin, Yiwen Zhang
Summary: This study proposes a privacy-preserving federated framework, called FedCDR, for addressing privacy leakage risks in cross-domain recommendation. The framework trains a recommendation model on each user's personal device and adopts local differential privacy to protect user privacy. It effectively solves the privacy issues caused by centralized storage of user data.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Computer Science, Hardware & Architecture
Can Zhang, Liehuang Zhu, Chang Xu, Zijian Zhang, Rongxing Lu
Summary: Blockchain-based covert communication has opened up a new research direction, providing reliable and undetectable storage methods. However, existing solutions using pre-negotiated and fixed addresses as static labels increase the risk of channel exposure and lack support for large covert message transmission. This paper presents an effective solution with dynamic labels and a message segmentation mechanism, achieving efficient extraction of covert messages and secure transmission of large covert messages.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Chang Xu, Run Yin, Liehuang Zhu, Can Zhang, Kashif Sharif
Summary: The increase in popularity and usage of the Internet of Things (IoT) applications, along with big data, has highlighted the importance of time-series data aggregation. However, existing privacy-preserving solutions face challenges in fault tolerance and establishment of trusted authorities. This paper proposes a privacy-preserving time-series data aggregation scheme without trusted authorities, showcasing its reliability and scalability.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2023)
Article
Mathematics
Dong Han, Zhen Li, Mengyu Wang, Chang Xu, Kashif Sharif
Summary: This paper presents a concrete GSH protocol with Multiple Groups, which provides affiliation-hiding and detectability while achieving Perfect Forward Secrecy. Only legitimate members can prove their affiliations without revealing specific group identities.
Article
Engineering, Electrical & Electronic
Liehuang Zhu, Md Monjurul Karim, Kashif Sharif, Chang Xu, Fan Li
Summary: This article proposes a UAV-assisted multi-layer IC-SDN solution to address content distribution challenges in UAV-assisted networks. It introduces distributed controllers placed hierarchically in the edge and cloud tiers and formulates the traffic optimization problem using a queueing allocation model. The proposed solution maximizes throughput and minimizes computational latency, delay, and packet loss, as shown through simulation and numerical evaluation.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Tong Wu, Weijie Wang, Chuan Zhang, Weiting Zhang, Liehuang Zhu, Keke Gai, Haotian Wang
Summary: This article proposes a blockchain-based anonymous data-sharing scheme (BA-DS) that adopts a novel public key encryption derived from a ring signature. BA-DS removes the trusted party and ensures anonymity by using an unconditional linkable ring signature and Signature of Knowledge (SoK). It applies blockchain infrastructure for revocation and generates tags for data stored on the cloud, providing accountability. The formal security analysis shows that BA-DS is selectively indistinguishable secure in the random oracle model. It also proves that BA-DS achieves anonymity, data privacy, accountability, and authenticity. Extensive experiments demonstrate reasonable efficiency of BA-DS in terms of computational complexity, communication overhead, and blockchain consumption.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Chang Xu, Ruijuan Wang, Liehuang Zhu, Chuan Zhang, Rongxing Lu, Kashif Sharif
Summary: Searchable symmetric encryption (SSE) allows keyword search on encrypted data. Dynamic searchable symmetric encryption (DSSE) enables data updating. Existing DSSE schemes suffer from keyword pair result pattern (KPRP) leakage. We propose the first DSSE scheme that achieves strong privacy-preserving conjunctive keyword search.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Theory & Methods
Liehuang Zhu, Yumeng Xie, Yuao Zhou, Qing Fan, Chuan Zhang, Ximeng Liu
Summary: The Healthcare Internet of Things system allows for remote monitoring of patient health and timely feedback on medical treatments. This paper proposes an efficient and secure health data sharing scheme called ESDS for Healthcare IoT systems. ESDS utilizes signcryption to protect patient privacy and ensure data authenticity, aggregates multiple signatures into a short signature, and applies local verifiability to improve system verification efficiency.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Theory & Methods
Haotian Liang, Youqi Li, Chuan Zhang, Ximeng Liu, Liehuang Zhu
Summary: Despite the state-of-the-art performance of federated learning in distributed learning tasks with privacy requirements, it is vulnerable to adversarial attacks. This paper proposes a novel method, External Gradient Inversion Attack (EGIA), which considers the transmission of public-shared gradients through intermediary nodes in the grey-box settings. The experiments demonstrate that an external adversary can reconstruct the private input using gradients even when both the clients and the server are honest and fully trusted.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Theory & Methods
Chenfei Hu, Chuan Zhang, Dian Lei, Tong Wu, Ximeng Liu, Liehuang Zhu
Summary: In this paper, the first privacy-preserving and verifiable support vector machine training scheme is proposed by employing a two-cloud platform. A verification mechanism is designed based on the homomorphic verification tag to enable verifiable machine learning training. Meanwhile, an efficient multiplication operation in the encryption domain is designed by combining homomorphic encryption and data perturbation to improve the efficiency of model training. Rigorous theoretical analysis demonstrates the security and reliability of the scheme. Experimental results show that the scheme can reduce computational and communication overheads by at least 43.94% and 99.58%, respectively, compared to state-of-the-art SVM training methods.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Hardware & Architecture
Zihan Li, Mingyang Zhao, Guanyu Chen, Chuan Zhang, Tong Wu, Liehuang Zhu
Summary: This paper proposes an efficient and privacy-preserving versatile task allocation scheme for the Internet of Vehicles, utilizing randomizable matrix multiplication and polynomial fitting techniques.
IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY
(2022)
Article
Computer Science, Theory & Methods
Chuan Zhang, Mingyang Zhao, Liehuang Zhu, Tong Wu, Ximeng Liu
Summary: In this paper, we propose an efficient and strong privacy-preserving truth discovery scheme, named EPTD, to protect users' task privacy and data privacy simultaneously in the truth discovery procedure.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Hardware & Architecture
Chang Xu, Yayun Si, Liehuang Zhu, Chuan Zhang, Kashif Sharif, Huishu Wu
Summary: This paper investigates the problem of dynamic data transaction in crowdsensing. The contribution of new data to the collector is modeled using the Shapley value, and a contextual bandit model is used to determine the transaction price. Simulation experiments demonstrate the effectiveness of the proposed scheme.
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
(2022)
Proceedings Paper
Computer Science, Software Engineering
Chang Xu, Hongzhou Rao, Liehuang Zhu, Chuan Zhang, Kashif Sharif
Summary: This paper proposes a novel scheme named V-EPTD that not only protects privacy information but also verifies the computing in truth discovery. The experimentation and analysis show that V-EPTD has good performances for users, verifiers, and the server, both in communication overhead and computation overhead.
ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2021, PT III
(2022)
Proceedings Paper
Computer Science, Information Systems
Yujia Tang, Chang Xu, Can Zhang, Yan Wu, Liehuang Zhu
Summary: Tornado Cash, a popular non-custodial coin mixer on Ethereum, is used to protect the privacy of addresses. However, it has privacy leakage risks due to inappropriate transaction behaviors. This paper analyzes the privacy issues of Tornado Cash and proposes clustering rules to reduce the size of users' anonymity set.
CYBER SECURITY, CNCERT 2021
(2022)
