Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring by Eugenia Tîrziu, Ana-Mihaela Vasilevschi, Adriana Alexandru, Eleonora Tudora

“…Furthermore, the approach ensures data privacy by processing only skeletal points derived from pose estimation, with no personal data stored. …”
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Optimising data sharing whilst protecting participant privacy: a data note describing processed data from a qualitative study of healthcare professionals’ experiences of caring for... by Hannah A. Long, Peter Branney, David P. French, Joanna M. Brooks

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Research on algorithms of data encryption scheme that supports homomorphic arithmetical operations by ANGPan Y, UIXiao-lin G, AOJing Y, INJian-cai L, IANFeng T, HANGXue-jun Z

“…An efficient homomorphic encryption scheme called CESIL was proposed to meet the requirements of operating on encrypted data when protecting users' privacy in computing services.CESIL included key generation algorithm,encryption algorithm,decryption algorithm and calculation algorithm.In CESIL,a polynomial coefficient vector ring was established by defining addition and multiplication using polynomial ring; by using ideal lattice,the vector ring was partitioned into many residue classes to produce a quotient ring and its representative set; the plaintext was encrypted by mapping it to a representative and replacing the representative with another element in the same residue class.The features of operations in quotient ring ensured CESIL operate on encrypted data.Furthermore,the fast Fourier transform (FFT) algorithm was used to increase the efficiency and decrease the length of key.Theoretical analysis and experimental results show that CESIL is semantically secure,and can do addition and multiplication operations on encrypted data homomorphically in a specific scope.Comparing to some existing homomorphic encryption schemes,the CESIL runs efficiently,and has shorter length in key and ciphertext.Thus,the CESIL fits the practical applications better.…”
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Building Equi-Width Histograms on Homomorphically Encrypted Data by Dragoș Lazea, Anca Hangan, Tudor Cioara

“…Histograms are widely used for summarizing data distributions, detecting anomalies, and improving machine learning models’ accuracy. …”
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Personal Information Sharing Behavior Using Social Media by Ashraf Sharif, Shafiq Ur Rehman, Khalid Mahmood

“…This study explores personal information sharing behavior publication patterns and trends on social media from 2007-2024 with an aim to highlight the annual growth of personal information sharing behavior (PISB) on social media platforms, key patterns in the PISB literature in terms of frequently cited authors, countries, institutions, sources, highly cited papers, collaboration and authorship patterns, thematic evolution, keyword and key factor analysis (such as countries, sources, and keywords). We used Scopus database for data extraction, and 1020 pertinent records were chosen. …”
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Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach by Xianwen Fang, Mengyao Li

“…Deeper integration of cross-organizational business process sharing and process mining has advanced the Industrial Internet. Privacy breaches and data security risks limit its use. …”
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Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption by Kamran Saeed, M.Fatih Adak

“…Self-driving vehicles leverage internet of things (IoT) technology to utilize multiple sensors to continuously monitor their environment and make decisions without human intervention. Data collected from these sensors require secure transmission and encrypted storage in cloud servers, where retrieving the data without decryption is necessary to ensure privacy. …”
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Assessing the Fidelity and Utility of Water Systems Data Using Generative Adversarial Networks: A Technical Review by Md Nazmul Kabir Sikder, Yingjie Wang, Feras A. Batarseh

“…This review paper addresses this limitation by utilizing Generative Adversarial Networks (GANs) to generate realistic synthetic datasets, overcoming data scarcity and privacy concerns in WDSs. We review, train, and evaluate seven state-of-the-art GAN models using three multivariate time-series datasets. …”
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Decentralized Blockchain-Based Authentication and Interplanetary File System-Based Data Management Protocol for Internet of Things Using Ascon by Hiba Belfqih, Abderrahim Abdellaoui

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Travel Behavior during the 2021 British Columbia Floods Using De-identified Network Mobility Data by Enqi Liao, Syeda N. Zehra, Stephen D. Wong

“…This study investigates residents’ emergent travel behavior before, during, and after the 2021 British Columbia Floods. Using de-identified network mobility data, we analyze travel patterns centered around the municipality of Hope in British Columbia, Canada. …”
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Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data by Shehu Mohammed, Neha Malhotra

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AN APPROACH PRIORITIZING THE CAUSAL FACTORS OF LARGE SCALED DATA USING SOFT COMPUTING: A CASE STUDY by Jyoti Prakash MISHRA, Zdzislaw POLKOWSKI, Sambit Kumar MISHRA

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Does data privacy influence digital marketing? The mediating role of AI-driven trust: An empirical study of Zain Telecom company in Jordan by Nidal Al Said

“…The PLS-SEM technique was used in this research to analyze data privacy, digital marketing effectiveness, and AI-driven trust constructs. …”
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Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic Encryption by Filip Jerkovic, Nurul I. Sarkar, Jahan Ali

“…Homomorphic Encryption (HE) introduces new dimensions of security and privacy within federated learning (FL) and internet of things (IoT) frameworks that allow preservation of user privacy when handling data for FL occurring in Smart Grid (SG) technologies. …”
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Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection by Mahesh Vaijainthymala Krishnamoorthy

“… Abstract BackgroundThe increasing integration of artificial intelligence (AI) systems into critical societal sectors has created an urgent demand for robust privacy-preserving methods. Traditional approaches such as differential privacy and homomorphic encryption often struggle to maintain an effective balance between protecting sensitive information and preserving data utility for AI applications. …”
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How to use relevant data for maximal benefit with minimal risk: digital health data governance to protect vulnerable populations in low-income and middle-income countries by Nicki Tiffin, Asha George, Amnesty Elizabeth LeFevre

“…Globally, the volume of private and personal digital data has massively increased, accompanied by rapid expansion in the generation and use of digital health data. …”
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Analyzing the vulnerabilities in Split Federated Learning: assessing the robustness against data poisoning attacks by Aysha-Thahsin Zahir-Ismail, Raj Shukla

“…Abstract Distributed Collaborative Machine Learning (DCML) offers a promising alternative to address privacy concerns in centralized machine learning. …”
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A comprehensive review of methodologies and application to use the real-world data and analytics platform TriNetX by Ralf J. Ludwig, Ralf J. Ludwig, Ralf J. Ludwig, Matthew Anson, Matthew Anson, Henner Zirpel, Diamant Thaci, Henning Olbrich, Katja Bieber, Khalaf Kridin, Khalaf Kridin, Khalaf Kridin, Astrid Dempfle, Philip Curman, Philip Curman, Philip Curman, Philip Curman, Sizheng S. Zhao, Uazman Alam, Uazman Alam, Uazman Alam

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FedSVD: Asynchronous Federated Learning With Stale Weight Vector Decomposition by Giwon Sur, Hyejin Kim, Seunghyun Yoon, Hyuk Lim

“…Federated learning (FL) emerges as a collaborative learning framework that addresses the critical needs for privacy preservation and communication efficiency. …”
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Scalable architecture for autonomous malware detection and defense in software-defined networks using federated learning approaches by Ripal Ranpara, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-Zahrani

“…This architecture combines SDN’s centralized management of potentially significant data streams with FL’s decentralized, privacy-preserving learning capabilities in a distributed manner adaptable to varying time and space constraints. …”
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