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Personalized Model Partitioning and Aggregation for Federated Human Activity Recognition
Weidong Zhang;Xuangou Wu;Tao Zhang;Xiujun Wang;Jiacheng Wang;Ruichen Zhang;Tong Wu;Jiawen Kang;Dusit Niyato
IEEE Transactions on Cognitive Communications and Networking
Year: 2025 | Early Access Article |
Federated learning-based human activity recognition (FL-HAR) has emerged as a significant research topic due to its extensive applications, enabling the collective integration of local client data and knowledge without compromising user privacy. A major challenge within FL-HAR is addressing the heterogeneity of data across distributed clients, which leads to varying feature distributions for the s...Show More
PriChain: Efficient Privacy-Preserving Fine-Grained Redactable Blockchains in Decentralized Settings
Hongchen Guo;Weilin Gan;Mingyang Zhao;Chuan Zhang;Tong Wu;Liehuang Zhu;Jingfeng Xue
Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching eff...Show More
A Scalable and Timely Data Auditing Scheme for Metaverse
2024 IEEE Smart World Congress (SWC)
Year: 2024 | Conference Paper |
The metaverse is a new trend in virtual reality applications, and data storage and management commonly rely on distributed storage systems. The integrity of the stored data has become a key concern in the metaverse. Furthermore, the scalability of data storage is a significant characteristic of the metaverse. Therefore, it is essential to provide a scalable data auditing approach for distributed o...Show More
Blockchain-based Privacy-preserving Data Service Provisioning for Internet of Things
2024 IEEE International Conference on Web Services (ICWS)
Year: 2024 | Conference Paper |
There are long-term concerns about the risk of privacy leakage when data is acquired and transmitted via Internet of Things (IoT) devices, which too often hinders the development of data-driven applications and services. To tackle such obstacle, we present a novel blockchain-based data service provisioning architecture based on federated learning, named BP-DSP. The system enables data owners to co...Show More
Accountable and Secure Threshold EdDSA Signature and Its Applications
Yumeng Xie;Qing Fan;Chuan Zhang;Tong Wu;Yuao Zhou;Debiao He;Liehuang Zhu
Threshold signatures as a method to realize multi-party cooperation and trust distribution in blockchain have been widely studied in recent years. However, among these researches, few threshold signature schemes achieve all the properties of accountability, privacy, and key protection for the EdDSA-based blockchain systems. To fill this gap, we propose an EdDSA-based accountable threshold signatur...Show More
Revocable and Privacy-Preserving Bilateral Access Control for Cloud Data Sharing
Mingyang Zhao;Chuan Zhang;Tong Wu;Jianbing Ni;Ximeng Liu;Liehuang Zhu
In this paper, we propose a revocable and privacy-preserving bilateral access control scheme (named PriBAC) for general cloud data sharing (i.e., end-cloud-based data sharing). PriBAC ensures that preference matching is successful only when both parties’ preferences are satisfied simultaneously. Otherwise, nothing is leaked beyond whether the preference matching occurs. There are three challenges ...Show More
Secure Decentralized Aggregation to Prevent Membership Privacy Leakage in Edge-Based Federated Learning
Meng Shen;Jing Wang;Jie Zhang;Qinglin Zhao;Bohan Peng;Tong Wu;Liehuang Zhu;Ke Xu
Federated Learning (FL) is a machine learning approach that enables multiple users to share their local models for the aggregation of a global model, protecting data privacy by avoiding the sharing of raw data. However, frequent parameter sharing between users and the aggregator can incur high risk of membership privacy leakage. In this paper, we propose LiPFed, a computationally lightweight priva...Show More
Toward Fine-Grained Task Allocation With Bilateral Access Control for Intelligent Transportation Systems
Tong Wu;Xiaochen Ma;Chuan Zhang;Ximeng Liu;Guomin Yang;Liehuang Zhu
IEEE Internet of Things Journal
Cited by: Papers (6)
In this article, we propose a secure fine-grained task allocation scheme with bilateral access control (FTA-BAC) for intelligent transportation systems. To enhance the security, we formulate bilateral access control in task allocation, by adopting the matchmaking encryption (ME) to encrypt the task requirements/interests for secure task matching. In this way, both task requesters and workers can s...Show More
Blockchain-Based Dynamic Time-Encapsulated Data Auditing for Outsourcing Storage
Chuan Zhang;Haojun Xuan;Tong Wu;Ximeng Liu;Guomin Yang;Liehuang Zhu
Outsourcing storage has emerged as an effective solution to manage the increasing volume of data. With the popularity of pay-as-you-go payment models in outsourcing storage, data auditing schemes that prioritize timeliness can be valuable evidence for elastic bill settlement. Unfortunately, existing data auditing schemes do not sufficiently consider timeliness during auditing. Furthermore, practic...Show More
Achieving Privacy-preserving Arbitrary Geometric Range Query with Bilateral Access Control
Xiaochen Ma;Chenfei Hu;Zhuopeng Li;Haotian Liang;Tong Wu;Taiyuan Zhang
2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Recently, the cloud server has been extensively applied to store large amounts of location data and provide geometric range query services. However, this may pose severe privacy concerns. Existing privacy-preserving geometric range query schemes either rely on high-cost encryption techniques or cannot support arbitrary geometric range query. Moreover, in these schemes, location data can be queried...Show More
Achieving Privacy-Preserving and Verifiable Support Vector Machine Training in the Cloud
Chenfei Hu;Chuan Zhang;Dian Lei;Tong Wu;Ximeng Liu;Liehuang Zhu
With the proliferation of machine learning, the cloud server has been employed to collect massive data and train machine learning models. Several privacy-preserving machine learning schemes have been suggested recently to guarantee data and model privacy in the cloud. However, these schemes either mandate the involvement of the data owner in model training or utilize high-cost cryptographic techni...Show More
Stealing Secrecy from Outside: A Novel Gradient Inversion Attack in Federated Learning
Chuan Zhang;Haotian Liang;Youqi Li;Tong Wu;Liehuang Zhu;Weiting Zhang
2022 IEEE 28th International Conference on Parallel and Distributed Systems (ICPADS)
Year: 2023 | Conference Paper |
Cited by: Papers (6)
Knowing model parameters has been regarded as a vital factor for recovering sensitive information from the gradients in federated learning. But is it safe to use federated learning when the model parameters are unavailable for adversaries, i.e., external adversaries’ In this paper, we answer this question by proposing a novel gradient inversion attack. Speciffically, we observe a widely ignored fa...Show More
Blockchain-Based Anonymous Data Sharing With Accountability for Internet of Things
Tong Wu;Weijie Wang;Chuan Zhang;Weiting Zhang;Liehuang Zhu;Keke Gai;Haotian Wang
IEEE Internet of Things Journal
Cited by: Papers (17)
Blockchain has been a promising infrastructure for enabling secure data sharing for the Internet of Things (IoT). With the widespread of IoT applications, security issues, such as data privacy, anonymity, and accountability become critical concerns for the users, which are essential principles for secure communication in those applications. However, the existing blockchain-based data-sharing schem...Show More
Achieving Efficient and Privacy-Preserving Neural Network Training and Prediction in Cloud Environments
The neural network has been widely used to train predictive models for applications such as image processing, disease prediction, and face recognition. To produce more accurate models, powerful third parties (e.g., clouds) are usually employed to collect data from a large number of users, which however may raise concerns about user privacy. In this paper, we propose an Efficient and Privacy-preser...Show More
Edge-based Dynamic Data Integrity Checking Scheme for IoT
2022 10th International Conference on Information Systems and Computing Technology (ISCTech)
Year: 2022 | Conference Paper |
The increasing data volume causes a challenging issue when storing the data locally in Internet of Things. Therefore, outsourcing the data to cloud service provider is treated as a potential solution for its low cost, high availability and scalability. In this case, users lose control to the outsourced data, facing severe data integrity challenge. Ensuring the integrity of outsourced data is an es...Show More
Achieving a Blockchain-based Privacy-preserving Quality-aware Knowledge Marketplace in Crowdsensing
Yanwei Li;Mingyang Zhao;Zihan Li;Weiting Zhang;Jinyang Dong;Tong Wu;Chuan Zhang;Liehuang Zhu
2022 IEEE 20th International Conference on Embedded and Ubiquitous Computing (EUC)
Year: 2022 | Conference Paper |
Cited by: Papers (6)
It is increasingly popular to utilize the wisdom of the crowd for knowledge discovery and monetization. Most of the existing knowledge marketplaces in crowdsensing are implemented by a third-party platform, which may compromise users' rights and be vulnerable to incurring attacks in practice. To eliminate the untrustworthy behaviors of the third party and improve tolerance for the attacks, some bl...Show More
Towards Secure Bilateral Friend Query with Conjunctive Policy Matching in Social Networks
Chuan Zhang;Mingyang Zhao;Tong Wu;Weiting Zhang;Qing Fan;Liehuang Zhu
Bilateral friend queries have attracted increasing interest in social networks, as each user has a common requirement to specify a policy for the other. However, existing bilateral friend query schemes either only cannot support conjunctive policy matching, where the match is successful if the policy is a subset of attributes, or compromise user privacy, which reduces users' enthusiasm for friend ...Show More
Achieving Efficient and Privacy-Preserving Arbitrary Geographic Range Query for Cloud
2022 4th International Conference on Data Intelligence and Security (ICDIS)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Geographic range query, as a basic query function, has been widely leveraged in location-based services. To adapt to the explosive growth of location data, more and more businesses and individuals choose to store their massive amounts of data on the powerful cloud, which however may raise severe threats to users' privacy. To resolve this problem, the location data is often encrypted before outsour...Show More
Towards Efficient and Privacy-Preserving Versatile Task Allocation for Internet of Vehicles
Zihan Li;Mingyang Zhao;Guanyu Chen;Chuan Zhang;Tong Wu;Liehuang Zhu
Nowadays, task allocation has attracted increasing attention in the Internet of Vehicles. To efficiently allocate tasks to suitable workers, users usually need to publish their task interests to the service provider, which brings a serious threat to users' privacy. Existing task allocation schemes either cannot comprehensively preserve user privacy (i.e., requester privacy and worker privacy) or i...Show More
FRUIT: A Blockchain-Based Efficient and Privacy-Preserving Quality-Aware Incentive Scheme
Chuan Zhang;Mingyang Zhao;Liehuang Zhu;Weiting Zhang;Tong Wu;Jianbing Ni
Incentive plays an important role in knowledge discovery, as it impels users to provide high-quality knowledge. To promise incentive schemes with transparency, blockchain technology has been widely used in incentive schemes. Currently, privacy, reliability, streamlined processing, and quality awareness are major challenges in designing blockchain-based incentive schemes. In this paper, we design a...Show More
Achieving Privacy-preserving Data Sharing for Dual Clouds
2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
With the advent of the era of Internet of Things (IoT), the increasing data volume leads to storage outsourcing as a new trend for enterprises and individuals. However, data breaches frequently occur, bringing significant challenges to the privacy protection of the outsourced data management system. There is an urgent need for efficient and secure data sharing schemes for the outsourced data manag...Show More
Enabling Efficient and Strong Privacy-Preserving Truth Discovery in Mobile Crowdsensing
Mobile crowdsensing has emerged as a popular platform to solve many challenging problems by utilizing users’ wisdom and resources. Due to user diversity, the data provided by different individuals may vary significantly, and thus it is important to analyze data quality during data aggregation. Truth discovery is effective in capturing data quality and obtaining accurate mobile crowdsensing results...Show More
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