BFG: privacy protection framework for internet of medical things based on blockchain and federated learning by Wenkang Liu, Yuxuan He, Xiaoliang Wang, Ziming Duan, Wei Liang, Yuzhen Liu

“…In this paper, we design a new privacy protection framework (BFG) for decentralized FL using blockchain, differential privacy and Generative Adversarial Network. …”
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A privacy budget adaptive optimization scheme for federated computing power Internet of things by MA Wenyu, CHEN Qian, HU Yuxiang, YAN Haonan, HU Tao, YI Peng

“…Furthermore, by assessing the similarity between the local model and the aggregated model, as well as their respective privacy budget proportions, the global contribution of each node was determined, which was used to fairly, also in real time, optimize and adjust the privacy budget settings in conjunction with the estimated privacy budget. …”
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Federated Learning-Based Trust Evaluation With Fuzzy Logic for Privacy and Robustness in Fog Computing by Thinh Le Vinh, Huan Thien Tran, Huyen Trang Phan, Samia Le

“…This paper introduces a Federated Learning Trust Model (FLTM) to assess trustworthiness across 2000 resources while preserving data privacy. FLTM incorporates six critical metrics: Availability, Reliability, Data Integrity, Identity, Computational Capability, and Throughput. …”
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Securing smart cities through machine learning: A honeypot‐driven approach to attack detection in Internet of Things ecosystems by Yussuf Ahmed, Kehinde Beyioku, Mehdi Yousefi

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Touch of Privacy: A Homomorphic Encryption-Powered Deep Learning Framework for Fingerprint Authentication by U. Sumalatha, K. Krishna Prakasha, Srikanth Prabhu, Vinod C. Nayak

“…Deep learning and fully homomorphic encryption (FHE) are integrated for privacy-preserving fingerprint recognition. Convolutional neural network (CNN) extract fingerprint features encrypted using the Cheon-Kim-Kim-Song (CKKS) FHE scheme. …”
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Assessment method on protection effectiveness of differential privacy algorithms based on fuzzy influence diagram by TIAN Yuechi, LI Fenghua, ZHOU Zejun, SUN Zhe, GUO Shoukun, NIU Ben

“…Starting from five aspects—algorithm security, feasibility, privacy bias, data utility, and user experience, an indicator system was established. …”
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An Efficient Elliptic Curve Cryptography-Based Secure Communication with Privacy Preserving for Autonomous Vehicle by Jiongen Xiao, Yi Liu, Yi Zou, Dacheng Li, Tao Leng

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Quantum Privacy Comparison with <i>R_y</i> Rotation Operation by Min Hou, Yue Wu

“…This paper presents a novel quantum privacy comparison (QPC) protocol that employs <i>R_y</i> rotation operations to enable two participants to securely compare their binary secrets without disclosing the actual data to any party except for the comparison result. …”
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Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission by Mehdi Letafati, Seyyed Amirhossein Ameli Kalkhoran, Ecenaz Erdemir, Babak Hossein Khalaj, Hamid Behroozi, Deniz Gunduz

“…Furthermore, useful insights on the privacy-utility trade-off are also provided.…”
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Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning by Milad Rahmati

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Dataset of worker perceptions of workforce robotics regarding safety, independence, job security, and privacyGitHub by Yu (Andrew) Liu, Gurpreet Kaur, Natasha Kholgade Banerjee, Sean Banerjee

“…The questions range from worker demographics (7 questions), perceptions toward physical safety in the workplace (8 questions), perceptions toward working with human coworkers in the workplace (6 questions), perceptions toward working with robots (16 questions), and perceptions toward data privacy on robots (3 questions). The dataset will enable research on understanding worker concerns with sensing systems and data privacy in workforce robots and enable data informed recommendations on privacy and security preserving sensing systems on existing and future robots. …”
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Time Series Classification Using Federated Convolutional Neural Networks and Image-Based Representations by Felipe A. R. Silva, Omid Orang, Fabricio Javier Erazo-Costa, Petronio C. L. Silva, Pedro H. Barros, Ricardo P. M. Ferreira, Frederico Gadelha Guimaraes

“…Federated Learning (FL) enables collaborative training across distributed clients while ensuring data privacy. The method&#x2019;s effectiveness is evaluated using 40 datasets from the UCR Archive. …”
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Federated target trial emulation using distributed observational data for treatment effect estimation by Haoyang Li, Chengxi Zang, Zhenxing Xu, Weishen Pan, Suraj Rajendran, Yong Chen, Fei Wang

“…Abstract Target trial emulation (TTE) aims to estimate treatment effects by simulating randomized controlled trials using real-world observational data. Applying TTE across distributed datasets shows great promise in improving generalizability and power but is always infeasible due to privacy and data-sharing constraints. …”
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A review of quantum communication and information networks with advanced cryptographic applications using machine learning, deep learning techniques by R. Ramya, P. Kumar, D. Dhanasekaran, R. Satheesh Kumar, S. Amithesh Sharavan

“…Quantum-secured networks enhance urban security, smart management, and infrastructure protection by providing data privacy and facilitating secure data transmission, thereby facilitating robust infrastructure and technological development.…”
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Generating unseen diseases patient data using ontology enhanced generative adversarial networks by Chang Sun, Michel Dumontier

“…Abstract Generating realistic synthetic health data (e.g., electronic health records), holds promise for fundamental research, AI model development, and enhancing data privacy safeguards. …”
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Predicting financial default risks: A machine learning approach using smartphone data by Shinta Palupi, Gunawan, Ririn Kusdyawati, Richki Hardi, Rana Zabrina

“…This study leverages machine learning (ML) techniques to predict financial default risks using smartphone data, providing a novel approach to financial risk assessment. …”
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Sample selection using multi-task autoencoders in federated learning with non-IID data by Emre Ardıç, Yakup Genç

“…Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. However, its performance is often hindered by redundant, malicious, or abnormal samples, leading to model degradation and inefficiency. …”
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Federated Learning Model Using CBDPL for Medical Image Segmentation by K. Hemalatha, Shradhanjali Das

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Efficient Keyset Design for Neural Networks Using Homomorphic Encryption by Youyeon Joo, Seungjin Ha, Hyunyoung Oh, Yunheung Paek

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Stub Signature-Based Efficient Public Data Auditing System Using Dynamic Procedures in Cloud Computing by Paromita Goswami, Neetu Faujdar, Ghanshyam Singh, Kanta Prasad Sharma, Ajoy Kumar Khan, Somen Debnath

“…To address these issues, we suggest proposing a partial signature-based data auditing system so that both privacy and accuracy can be fortified while reducing the computational cost associated with auditing processes significantly. …”
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