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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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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Ethical and legal considerations governing use of health data for quality improvement and performance management: a scoping review of the perspectives of health professionals and a... by Tim Shaw, Kevin Leow, Ian Kerridge, Cameron Stewart, Anna Janssen, Kavisha Shah

“…Health administrators additionally emphasised the importance of good governance and data stewardship to improving professional engagement with performance data, but privacy remains a key barrier.Conclusions The growing interest in using health data for quality improvement and performance management requires health services to address barriers to utilisation of performance data. …”
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Determinants of Continuous Smartwatch Use and Data-Sharing Preferences With Physicians, Public Health Authorities, and Private Companies: Cross-Sectional Survey of Smartwatch Users by Anthony James Goodings, Kayode Philip Fadahunsi, Derjung M Tarn, Jennifer Lutomski, Allison Chhor, Frances Shiely, Patrick Henn, John O'Donoghue

“… Abstract BackgroundSmartwatches are widely adopted globally for tracking health metrics, offering potential for enhancing individual health care and public health efforts. Continuous use of the devices and users’ willingness to share the data collected are critical to realizing their full benefits. …”
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Advancing Early Detection of Major Depressive Disorder Using Multisite Functional Magnetic Resonance Imaging Data: Comparative Analysis of AI Models by Masab Mansoor, Kashif Ansari

“…MethodsWe used functional magnetic resonance imaging data from 1200 participants (600 with early-stage MDD and 600 healthy controls) across 3 public datasets. …”
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KL-FedDis: A federated learning approach with distribution information sharing using Kullback-Leibler divergence for non-IID data by Md. Rahad, Ruhan Shabab, Mohd. Sultan Ahammad, Md. Mahfuz Reza, Amit Karmaker, Md. Abir Hossain

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ChildDiffusion: Unlocking the Potential of Generative AI and Controllable Augmentations for Child Facial Data Using Stable Diffusion and Large Language Models by Muhammad Ali Farooq, Wang Yao, Peter Corcoran

“…In this research, we propose the ChildDiffusion framework, designed to generate photorealistic child facial data using diffusion models. The framework integrates intelligent augmentations via short text prompts, employs various image samplers, and leverages ControlNet for enhanced model conditioning. …”
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De novo non-canonical nanopore basecalling enables private communication using heavily-modified DNA data at single-molecule level by Qingyuan Fan, Xuyang Zhao, Junyao Li, Ronghui Liu, Ming Liu, Qishun Feng, Yanping Long, Yang Fu, Jixian Zhai, Qing Pan, Yi Li

“…Abstract Hidden messages in DNA molecules by employing chemical modifications has been suggested for private data storage and transmission at high information density. …”
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Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data by Jennifer K Quint, Aziz Sheikh, Chris Robertson, Srinivasa Vittal Katikireddi, Emily Moore, Colin R Simpson, Luke Daines, Eleftheria Vasileiou, Syed Ahmar Shah, Rachel H Mulholland, Vicky Hammersley, Steven Kerr, Ting Shi, David Weatherill, Elisa Pesenti

“…In this protocol, we describe plans to develop a prediction model to identify individuals at risk of developing long-COVID.Methods and analysis We will use the national Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, a population-level linked dataset of routine electronic healthcare data from 5.4 million individuals in Scotland. …”
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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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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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Goalie: Defending Against Correlated Value and Sign Encoding Attacks by Rongfei Zhuang, Ximing Fu, Chuanyi Liu, Peiyi Han, Shaoming Duan

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Using fuzzy decision support to create a positive mental health environment for preschoolers by Xinyue Li

“…To assess their mental health before starting school, preschoolers need early detection, intervention, and assessment. However, data shortages, heterogeneity, privacy issues, model interpretability, and generalization restrictions hamper the review process. …”
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Impact of Normalization Techniques on Synthetic Load Profile Generation Using Deep Generative Models by Luis H. T. Bandoria, Walquiria N. Silva, Madson C. De Almeida

“…However, the impact of data normalization on their performance remains insufficiently explored. …”
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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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