A Verifiable, Privacy-Preserving, and Poisoning Attack-Resilient Federated Learning Framework by Washington Enyinna Mbonu, Carsten Maple, Gregory Epiphaniou, Christo Panchev

“…Federated learning is the on-device, collaborative training of a global model that can be utilized to support the privacy preservation of participants’ local data. …”
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Privacy-Preserving Live Video Analytics for Drones via Edge Computing by Piyush Nagasubramaniam, Chen Wu, Yuanyi Sun, Neeraj Karamchandani, Sencun Zhu, Yongzhong He

“…While edge computing offers a solution to the throughput bottleneck, it also opens the door to potential privacy invasions by exposing sensitive visual data to risks. …”
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Privacy preserving method based on Voronoi diagram in mobile crowd computing by Hao Long, Shukui Zhang, Jin Wang, Cheng-Kuan Lin, Jia-Jun Cheng

“…In the application, the publishers use the application platform to release the task and then select the appropriate users to participate in the task by bidding and collect their data, in which the users’ identity, location, and other private information face the risk of disclosure. …”
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Taking disagreements into consideration: human annotation variability in privacy policy analysis by Tian Wang, Yuanye Ma, Catherine Blake, Masooda Bashir, Hsin-Yuan Wang

“… Introduction. Privacy policies inform users about data practices but are often complex and difficult to interpret. …”
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A comprehensive review on the users’ identity privacy for 5G networks by Mamoon M. Saeed, Mohammad Kamrul Hasan, Ahmed J. Obaid, Rashid A. Saeed, Rania A. Mokhtar, Elmustafa Sayed Ali, Md Akhtaruzzaman, Sanaz Amanlou, A. K. M. Zakir Hossain

“…Abstract Fifth Generation (5G) is the final generation in mobile communications, with minimum latency, high data throughput, and extra coverage. The 5G network must guarantee very good security and privacy levels for all users for these features. …”
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Transparent and Privacy-Preserving Mobile Crowd-Sensing System with Truth Discovery by Ruijuan Jia, Juan Ma, Ziyin You, Mingyue Zhang

“…This scheme enables data requesters to effectively verify the correctness of the truth discovery service while ensuring data privacy. …”
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An Exact Top- Query Algorithm with Privacy Protection in Wireless Sensor Networks by Huang Haiping, Feng Juan, Wang Ruchuan, Qin XiaoLin

“…The algorithm does the query exactly and meanwhile uses conic section privacy function to prevent the disclosure of the real data and then to promise the security of nodes in network. …”
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Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning by Raymond Jiang, Yulia Kumar, Dov Kruger

“…However, due to the many privacy regulations regarding personal data, pooling together data from multiple sources and storing them in a single (centralized) location for traditional ML model training are often infeasible. …”
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A location semantic privacy protection model based on spatial influence by Linghong Kuang, Wenlong Shi, Xueqi Chen, Jing Zhang, Huaxiong Liao

“…Nonetheless, while trajectory data mining enhances user convenience, it also exposes their privacy to potential breaches. …”
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Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models by Baris Balaban, Seyma Selcan Magara, Caglar Yilgor, Altug Yucekul, Ibrahim Obeid, Javier Pizones, Frank Kleinstueck, Francisco Javier Sanchez Perez-Grueso, Ferran Pellise, Ahmet Alanay, Erkay Savas, Cetin Bagci, Osman Ugur Sezerman

“…Ensuring the security of both the model and the user data enables the protection of the intellectual property of ML models, preventing the leakage of sensitive information used in training and model users’ data.INDEX TERMS Homomorphic encryption, privacy-preserving machine learning, XGBoost.…”
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A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining by Ibrahim Ileri, Tugba Gurgen Erdogan, Ayca Kolukisa-Tarhan

“…Process mining (PM) is a growing field that looks at how to find, analyze, and improve process models using data from information systems. It automates much of the detailed work that usually requires a lot of manual effort. …”
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Privacy-enhanced federated learning scheme based on generative adversarial networks by Feng YU, Qingxin LIN, Hui LIN, Xiaoding WANG

“…Federated learning, a distributed machine learning paradigm, has gained a lot of attention due to its inherent privacy protection capability and heterogeneous collaboration.However, recent studies have revealed a potential privacy risk known as “gradient leakage”, where the gradients can be used to determine whether a data record with a specific property is included in another participant’s batch, thereby exposing the participant’s training data.Current privacy-enhanced federated learning methods may have drawbacks such as reduced accuracy, computational overhead, or new insecurity factors.To address this issue, a differential privacy-enhanced generative adversarial network model was proposed, which introduced an identifier into vanilla GAN, thus enabling the input data to be approached while satisfying differential privacy constraints.Then this model was applied to the federated learning framework, to improve the privacy protection capability without compromising model accuracy.The proposed method was verified through simulations under the client/server (C/S) federated learning architecture and was found to balance data privacy and practicality effectively compared with the DP-SGD method.Besides, the usability of the proposed model was theoretically analyzed under a peer-to-peer (P2P) architecture, and future research work was discussed.…”
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Edge computing privacy protection method based on blockchain and federated learning by Chen FANG, Yuanbo GUO, Yifeng WANG, Yongjin HU, Jiali MA, Han ZHANG, Yangyang HU

“…Aiming at the needs of edge computing for data privacy, the correctness of calculation results and the auditability of data processing, a privacy protection method for edge computing based on blockchain and federated learning was proposed, which can realize collaborative training with multiple devices at the edge of the network without a trusted environment and special hardware facilities.The blockchain was used to endow the edge computing with features such as tamper-proof and resistance to single-point-of-failure attacks, and the gradient verification and incentive mechanism were incorporated into the consensus protocol to encourage more local devices to honestly contribute computing power and data to the federated learning.For the potential privacy leakage problems caused by sharing model parameters, an adaptive differential privacy mechanism was designed to protect parameter privacy while reducing the impact of noise on the model accuracy, and moments accountant was used to accurately track the privacy loss during the training process.Experimental results show that the proposed method can resist 30% of poisoning attacks, and can achieve privacy protection with high model accuracy, and is suitable for edge computing scenarios that require high level of security and accuracy.…”
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Privacy protection risk identification mechanism based on automated feature combination by CAI Minchao, YAO Hongwei, WANG Yang, QIN Zhan, CHEN Shaomeng, REN Kui

“…Building upon the privacy protection method using homomorphic encryption, the technical challenge of optimizing feature combinations was addressed. …”
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Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory by Naoki Masuyama, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota

“…In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. However, most of the base clusterers (i.e., clustering algorithms) used in existing federated clustering algorithms need to specify the number of clusters in advance. …”
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A New Algorithm for Privacy-Preserving Horizontally Partitioned Linear Programs by Chengxue Zhang, Debin Kong, Peng Pan, Mingyuan Zhou

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ZK-STARK: Mathematical Foundations and Applications in Blockchain Supply Chain Privacy by Arade Madhuri S., Pise Nitin N.

“…Privacy is one of the major security concerns. The zero-knowledge proof enables the transmission of data from the sender to the receiver without disclosing the actual content of the data. …”
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Blockchain-based privacy-preserving multi-tasks federated learning framework by Yunyan Jia, Ling Xiong, Yu Fan, Wei Liang, Neal Xiong, Fengjun Xiao

“…To overcome this weakness, this work proposes a privacy-preserving FL framework with multi-tasks using partitioned blockchain, which can run several different FL tasks by multiple requesters. …”
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Privacy and Security in Digital Health Contact-Tracing: A Narrative Review by Shehani Pigera, Paul van Schaik, Karen Renaud, Miglena Campbell, Petra Manley, Pierre Esser

“…A total of 114 articles were retained as per the inclusion criteria, which included quantitative, qualitative, and mixed-methods studies. The data were analysed using thematic analysis. (3) Results: Eight main themes were derived: privacy, data protection and control, trust, technical issues, perceived benefit, knowledge and awareness, social influence, and psychological factors. (4) Conclusions: Improving privacy standards and the awareness of the digital contact-tracing process will encourage the acceptance of contact-tracing apps.…”
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A deep decentralized privacy-preservation framework for online social networks by Samuel Akwasi Frimpong, Mu Han, Emmanuel Kwame Effah, Joseph Kwame Adjei, Isaac Hanson, Percy Brown

“…This paper addresses the critical challenge of privacy in Online Social Networks (OSNs), where centralized designs compromise user privacy. …”
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