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Petya Campbell - IEEE Xplore Author Profile
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Petya Campbell

Also published under: Petya K. E. Campbell, P. K. E. Campbell, Petya K. Entcheva Campbell, P. Campbell, Petya E. Campbell, P. K. Entcheva Campbell

Publications
28
Citations
242
Publications by Year
20022021
Co-Authors:
Show All Co-Authors (65)
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Petya Campbell

Also published under: Petya K. E. Campbell, P. K. E. Campbell, Petya K. Entcheva Campbell, P. Campbell, Petya E. Campbell, P. K. Entcheva Campbell

Affiliation

University of Maryland Baltimore County (UMBC) and NASA Goddard Space Flight Center (GSFC)

Publication Topics

Biography

Petya K. Entcheva Campbell (M'99) received the B.S. degree in forest engineering and silviculture from the University of Forestry, Sofia, Bulgaria, the M.S. degree in forest ecology from the University of Massachusetts, Amherst, MA, USA, and the Ph.D. degree in forest analysis/remote sensing from University of New Hampshire, Durham, NH, USA.,Currently she is a Research Assistant Professor at the Joint Center for Earth Systems Technology, University of Maryland Baltimore County (UMBC). She is affiliated with the Department of Geography and Environmental Systems at UMBC, where she has taught GES481/681 undergraduate/graduate classes in “Remote sensing for environmental applications”. She is a research associate at the Biospheric Sciences Labo...
Publications
28
Citations
242
Publications by Year
20022021
Co-Authors:
Show All Co-Authors (65)
Author's Published Works

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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

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

Year: 2025 | Early Access Article |

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

Chinese Journal of Electronics
Year: 2025 | Volume: 34, Issue: 1 | Journal Article |
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

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

Year: 2025 | Volume: 34, Issue: 1 | Journal Article |

A Scalable and Timely Data Auditing Scheme for Metaverse

Chen Chen;Tong Wu;Chao Ren;Changai Sun

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

A Scalable and Timely Data Auditing Scheme for Metaverse

Chen Chen;Tong Wu;Chao Ren;Changai Sun

Year: 2024 | Conference Paper |

Blockchain-based Privacy-preserving Data Service Provisioning for Internet of Things

Jiamei Liu;Chang-ai Sun;Tong Wu;Marco Aiello

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

Blockchain-based Privacy-preserving Data Service Provisioning for Internet of Things

Jiamei Liu;Chang-ai Sun;Tong Wu;Marco Aiello

Year: 2024 | Conference Paper |

Accountable and Secure Threshold EdDSA Signature and Its Applications

Yumeng Xie;Qing Fan;Chuan Zhang;Tong Wu;Yuao Zhou;Debiao He;Liehuang Zhu

IEEE Transactions on Information Forensics and Security
Year: 2024 | Volume: 19 | Journal Article |
Cited by: Papers (1)
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

Accountable and Secure Threshold EdDSA Signature and Its Applications

Yumeng Xie;Qing Fan;Chuan Zhang;Tong Wu;Yuao Zhou;Debiao He;Liehuang Zhu

Year: 2024 | Volume: 19 | Journal Article |

Revocable and Privacy-Preserving Bilateral Access Control for Cloud Data Sharing

Mingyang Zhao;Chuan Zhang;Tong Wu;Jianbing Ni;Ximeng Liu;Liehuang Zhu

IEEE Transactions on Information Forensics and Security
Year: 2024 | Volume: 19 | Journal Article |
Cited by: Papers (4)
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

Revocable and Privacy-Preserving Bilateral Access Control for Cloud Data Sharing

Mingyang Zhao;Chuan Zhang;Tong Wu;Jianbing Ni;Ximeng Liu;Liehuang Zhu

Year: 2024 | Volume: 19 | Journal Article |

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

IEEE Transactions on Network Science and Engineering
Year: 2024 | Volume: 11, Issue: 3 | Journal Article |
Cited by: Papers (6)
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

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

Year: 2024 | Volume: 11, Issue: 3 | Journal Article |

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
Year: 2024 | Volume: 11, Issue: 8 | Journal Article |
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

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

Year: 2024 | Volume: 11, Issue: 8 | Journal Article |

Blockchain-Based Dynamic Time-Encapsulated Data Auditing for Outsourcing Storage

Chuan Zhang;Haojun Xuan;Tong Wu;Ximeng Liu;Guomin Yang;Liehuang Zhu

IEEE Transactions on Information Forensics and Security
Year: 2024 | Volume: 19 | Journal Article |
Cited by: Papers (7)
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

Blockchain-Based Dynamic Time-Encapsulated Data Auditing for Outsourcing Storage

Chuan Zhang;Haojun Xuan;Tong Wu;Ximeng Liu;Guomin Yang;Liehuang Zhu

Year: 2024 | Volume: 19 | Journal Article |

Achieving Privacy-Preserving and Verifiable Support Vector Machine Training in the Cloud

Chenfei Hu;Chuan Zhang;Dian Lei;Tong Wu;Ximeng Liu;Liehuang Zhu

IEEE Transactions on Information Forensics and Security
Year: 2023 | Volume: 18 | Journal Article |
Cited by: Papers (46)
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

Achieving Privacy-Preserving and Verifiable Support Vector Machine Training in the Cloud

Chenfei Hu;Chuan Zhang;Dian Lei;Tong Wu;Ximeng Liu;Liehuang Zhu

Year: 2023 | Volume: 18 | Journal Article |

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

Stealing Secrecy from Outside: A Novel Gradient Inversion Attack in Federated Learning

Chuan Zhang;Haotian Liang;Youqi Li;Tong Wu;Liehuang Zhu;Weiting Zhang

Year: 2023 | Conference Paper |

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
Year: 2023 | Volume: 10, Issue: 6 | Journal Article |
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

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

Year: 2023 | Volume: 10, Issue: 6 | Journal Article |

Achieving Efficient and Privacy-Preserving Neural Network Training and Prediction in Cloud Environments

Chuan Zhang;Chenfei Hu;Tong Wu;Liehuang Zhu;Ximeng Liu

IEEE Transactions on Dependable and Secure Computing
Year: 2023 | Volume: 20, Issue: 5 | Journal Article |
Cited by: Papers (51)
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

Achieving Efficient and Privacy-Preserving Neural Network Training and Prediction in Cloud Environments

Chuan Zhang;Chenfei Hu;Tong Wu;Liehuang Zhu;Ximeng Liu

Year: 2023 | Volume: 20, Issue: 5 | Journal Article |

Edge-based Dynamic Data Integrity Checking Scheme for IoT

Puyuan Zheng;Tong Wu;Jingyang Dong

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

Edge-based Dynamic Data Integrity Checking Scheme for IoT

Puyuan Zheng;Tong Wu;Jingyang Dong

Year: 2022 | Conference Paper |

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

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

Year: 2022 | Conference Paper |

Towards Secure Bilateral Friend Query with Conjunctive Policy Matching in Social Networks

Chuan Zhang;Mingyang Zhao;Tong Wu;Weiting Zhang;Qing Fan;Liehuang Zhu

2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)
Year: 2022 | Conference Paper |
Cited by: Papers (7)
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

Towards Secure Bilateral Friend Query with Conjunctive Policy Matching in Social Networks

Chuan Zhang;Mingyang Zhao;Tong Wu;Weiting Zhang;Qing Fan;Liehuang Zhu

Year: 2022 | Conference Paper |

Achieving Efficient and Privacy-Preserving Arbitrary Geographic Range Query for Cloud

Chuan Zhang;Chenfei Hu;Mingyang Zhao;Yulin Wu;Tong Wu

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

Achieving Efficient and Privacy-Preserving Arbitrary Geographic Range Query for Cloud

Chuan Zhang;Chenfei Hu;Mingyang Zhao;Yulin Wu;Tong Wu

Year: 2022 | Conference Paper |

Towards Efficient and Privacy-Preserving Versatile Task Allocation for Internet of Vehicles

Zihan Li;Mingyang Zhao;Guanyu Chen;Chuan Zhang;Tong Wu;Liehuang Zhu

IEEE Open Journal of the Computer Society
Year: 2022 | Volume: 3 | Journal Article |
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

Towards Efficient and Privacy-Preserving Versatile Task Allocation for Internet of Vehicles

Zihan Li;Mingyang Zhao;Guanyu Chen;Chuan Zhang;Tong Wu;Liehuang Zhu

Year: 2022 | Volume: 3 | Journal Article |

FRUIT: A Blockchain-Based Efficient and Privacy-Preserving Quality-Aware Incentive Scheme

Chuan Zhang;Mingyang Zhao;Liehuang Zhu;Weiting Zhang;Tong Wu;Jianbing Ni

IEEE Journal on Selected Areas in Communications
Year: 2022 | Volume: 40, Issue: 12 | Journal Article |
Cited by: Papers (59)
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

FRUIT: A Blockchain-Based Efficient and Privacy-Preserving Quality-Aware Incentive Scheme

Chuan Zhang;Mingyang Zhao;Liehuang Zhu;Weiting Zhang;Tong Wu;Jianbing Ni

Year: 2022 | Volume: 40, Issue: 12 | Journal Article |

Achieving Privacy-preserving Data Sharing for Dual Clouds

Xingqi Luo;Haotian Wang;Jinyang Dong;Chuan Zhang;Tong Wu

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

Achieving Privacy-preserving Data Sharing for Dual Clouds

Xingqi Luo;Haotian Wang;Jinyang Dong;Chuan Zhang;Tong Wu

Year: 2022 | Conference Paper |

Enabling Efficient and Strong Privacy-Preserving Truth Discovery in Mobile Crowdsensing

Chuan Zhang;Mingyang Zhao;Liehuang Zhu;Tong Wu;Ximeng Liu

IEEE Transactions on Information Forensics and Security
Year: 2022 | Volume: 17 | Journal Article |
Cited by: Papers (39)
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

Enabling Efficient and Strong Privacy-Preserving Truth Discovery in Mobile Crowdsensing

Chuan Zhang;Mingyang Zhao;Liehuang Zhu;Tong Wu;Ximeng Liu

Year: 2022 | Volume: 17 | Journal Article |

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