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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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 Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles by Ugur Ilker Atmaca, Sayan Biswas, Carsten Maple, Catuscia Palamidessi

“…Simultaneously, personal data use for analytics is growing at an unprecedented rate, raising concerns for privacy. …”
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PPSC: High-Precision and Scalable Encrypted Privacy-Preserving Speech Classification by WANG Leilei, SONG Kao, ZHANG Yuanyuan, BI Renwan, XIONG Jinbo

“…Secondly, the PPSC scheme securely implements the fundamental modules such as the convolutional layer, ReLU layer, average pooling layer, fully connected layer, and Softmax layer. This ensures the privacy of speech data, speech classification models, and intermediate computing results. …”
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Ensuring the security and privacy of information in mobile health-care communication systems by Ademola Adesina, Kehinde Agbele, Ronald Februarie, Ademola Abidoye, Henry Nyongesa

“…The sensitivity of health-care information and its accessibility via the Internet and mobile technology systems is a cause for concern in these modern times. The privacy, integrity and confidentiality of a patient's data are key factors to be considered in the transmission of medical information for use by authorised health-care personnel. …”
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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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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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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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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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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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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 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 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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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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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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