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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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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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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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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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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Privacy risk adaptive access control model via evolutionary game by Hongfa DING, Changgen PENG, Youliang TIAN, Shuwen XIANG

“…Aiming at the problem that in the private sensitive date centralized and opening information systems,a fine-grained and self-adaptive access control model for privacy preserving is desperately needed,thus the balance between privacy preserving and data access utility should be achieved,a rational multi-player risk-adaptive based access control model for privacy preserving was proposed.Firstly,the privacy risk values of access request and requester were formulized by the private information quantity of the requested dataset,and by using Shannon information.Secondly,a risk-adaptive based access control evolutionary game model was constructed by using evolutionary game under the supposing of bounded rational players.Furthermore,dynamic strategies of participants were analyzed by using replicator dynamics equation,and the method of choosing evolutionary stable strategy was proposed.Simulation and comparison results show that,the proposed model is effective to dynamically and adaptively preserve privacy and more risk adaptive,and dynamic evolutionary access strategies of the bounded rational participants are more suitable for practical scenarios.…”
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Biometric-based medical watermarking system for verifying privacy and source authentication by Nada Fadhil Mohammed, Majid Jabbar Jawad, Suhad Ahmed Ali

“…Two of the most requirements in e-health care system is the ensuring the authenticity of the source from which the data is received and the privacy of medical record of the patient must be preserved. …”
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Preserving privacy and video quality through remote physiological signal removal by Saksham Bhutani, Mohamed Elgendi, Carlo Menon

“…However, the continuous and surreptitious recording of individuals by these devices and the collecting of sensitive health data without users’ knowledge or consent raise serious privacy concerns. …”
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Verifiable secure aggregation scheme for privacy protection in federated learning networks by Wujun Yao, Tanping Zhou, Yiliang Han, Xiaolin Wang

“…Security analysis demonstrates that our solution effectively ensures privacy protection. We tested the performance using a Raspberry Pi as an edge computing device. …”
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Analysis, Design, and Implementation of a User-Friendly Differential Privacy Application by Reynardo Tjhin, Muhammad Sajjad Akbar, Clement Canonne, Rabia Bashir

“…In the era of artificial intelligence, ensuring privacy in publicly released data is critical to prevent linkage attacks that can reveal sensitive information about individuals. …”
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Digital citizenship literacy in Indonesia: The role of privacy awareness and social campaigns by Rossi Iskandar, Arifin Maksum, Arita Marini

“…A quantitative research approach was employed, using a survey method to collect data from 250 respondents of students from several high schools in Jakarta. …”
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Impact of perceived privacy and security in the TAM model: The perceived trust as the mediated factors by Yan Zhang

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Ethical and secure evidence generation from regionwide clinical data through a collaborative environment for advancing predictive care by Dolores Muñoyerro-Muñiz, Román Villegas, Víctor de la Oliva, Alberto Esteban-Medina, Patricia Fernández del Valle, Ana Sánchez, M. Belen Susin, Isidoro Gutierrez-Alvarez, Marta Reboredo, Laura Alejos, Carlos Loucera, Carlos Loucera, Joaquín Dopazo, Joaquín Dopazo

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Mining privacy-preserving association rules using transaction hewer allocator and facile hash algorithm in multi-cloud environments by D. Dhinakaran, S. Gopalakrishnan, D. Selvaraj, M.S. Girija, G. Prabaharan

“…However, in outsourced data for tasks like frequent itemset mining, privacy is an important issue. …”
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