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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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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Human Daily Indoor Action (HDIA) Dataset: Privacy-Preserving Human Action Recognition Using Infrared Camera and Wearable Armband Sensors by Jongbum Park, Kyoung Ok Yang, Sunme Park, Jun Won Choi

“…The use of IR sensors enhances privacy, making the dataset ethically suitable for long-term monitoring. …”
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Optimizing Customer Data Security in Water Meter Data Management Based on RESTful API and Data Encryption Using AES-256 Algorithm by Syahrul Adrianto, Bambang Agus Herlambang, Ramadhan Renaldy

“…To increase the security of customer data, a cryptographic algorithm is used, namely the Advanced Encryption Standard (AES) algorithm with a 256-bit key length to secure data that is considered sensitive and contains high privacy. …”
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STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion by Lei Tang, Zhengxin Cao, Xin Zhou, Junzhe Zhang, Junchi Ma

“…There are user privacy risks in cloud-based vehicle dispatch platforms due to the unauthorized collection, use, and dissemination of data. …”
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Recent Advances in Federated Learning for Connected Autonomous Vehicles: Addressing Privacy, Performance, and Scalability Challenges by Asad Ali, Huang Jianjun, Ayesha Jabbar

“…FL presents a decentralized infrastructure that allows collaborative learning, while also ensuring data privacy, as CAVs increasingly rely on machine learning to process large amounts of sensor data. …”
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Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN) by Komal Goyal, Megha Garg, Shruti Malik

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Energy-efficient and privacy-preserving spatial range aggregation query processing in wireless sensor networks by Liang Liu, Zhenhai Hu, Lisong Wang

“…The algorithm does not rely on the pre-established topology but considers only the query area that the user is interested in, abandoning all nodes to participate in distributing the query messages while gathering the sensory data in the query range. To protect node data privacy, Shamir’s secret sharing technology is used to prevent internal attackers from stealing the sensitive data of the surrounding nodes. …”
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Blockchain-Enabled Federated Learning to Enhance Security and Privacy in Internet of Medical Things (IoMT) by zahra eskandari

“…Federated learning is a distributed data analysis approach used in many IoT applications, including IoMT, due to its ability to provide acceptable accuracy and privacy. …”
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A Collaborative Framework for Privacy Preserving Fuzzy Co-Clustering of Vertically Distributed Cooccurrence Matrices by Katsuhiro Honda, Toshiya Oda, Daiji Tanaka, Akira Notsu

“…In many real world data analysis tasks, it is expected that we can get much more useful knowledge by utilizing multiple databases stored in different organizations, such as cooperation groups, state organs, and allied countries. …”
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Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing by Nada Alasbali, Jawad Ahmad, Ali Akbar Siddique, Oumaima Saidani, Alanoud Al Mazroa, Asif Raza, Rahmat Ullah, Muhammad Shahbaz Khan

“…Most existing automated detection/classification approaches that utilize machine learning or deep learning poses privacy issues, as they involve centralized computing and require local storage for data training.MethodsKeeping the privacy of sensitive patient data as a primary objective, in addition to ensuring accuracy and efficiency, this paper presents an algorithm that integrates Federated learning techniques into an IoT-based edge-computing environment. …”
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Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection by Amogh Deshmukh, Peplluis Esteva de la Rosa, Raul Villamarin Rodriguez, Sandeep Dasari

“…These methods work with data locality during training at local clients without exposing data, while maintaining global convergence to enhance the privacy of local models within the framework. …”
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Shielding Communication Privacy: Unveiling The Strategic Utilization Of Instagram’s Second Account Feature By Millennial Generation by Musfiah Saidah

“…Even though the person who is selected to enter the second account circle is also vulnerable to opening the data privacy of the account owner and even spreading it. …”
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