Cultural Differences in the Use of Augmented Reality Smart Glasses (ARSGs) Between the U.S. and South Korea: Privacy Concerns and the Technology Acceptance Model by Se Jung Kim, Yoon Esther Lee, T. Makana Chock

“…Korea = 898) perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention to use ARSGs were impacted by privacy concerns. …”
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A remote sensing encrypted data search method based on a novel double-chain by Xixi Yan, Pei Yin, Yongli Tang, Suwei Feng

“…However, with the rapid urbanisation and the increase in data capacity, how to store and retrieve remote sensing data which have more potential value and privacy with a more effective and safe method has become an imminent issue. …”
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TrapMI: A Data Protection Method to Resist Model Inversion Attacks in Split Learning by Hyunsik Na, Daeseon Choi

“…However, concerns remain regarding data privacy leakage because an attacker can still attempt model inversion attacks based on the intermediate features. …”
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Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework by Jin Lei, Weiyun Li, Meng Yue, Zhijun Wu

“…FedMeta-CTGAN exploits the natural privacy-preserving properties of a two-stage update in meta-learning, using real data to train the CTGAN model and synthetic fake data as query data during meta-training. …”
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A Novel Linearly Complex Extended Signed Response-Based Node Authentication Scheme for Internet of Medical Things by Muhammad Saud Khan, Muhammad Sardaraz, Muhammad Tahir, Abdullah Alourani

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ZSS Signature-Based Audit Message Verification Process for Cloud Data Integrity by Paromita Goswami, Neetu Faujdar, Somen Debnath, Ajoy Kumar Khan, Ghanshyam Singh

“…Instead of establishing a secure communication route between all parties, a progressive technique is therefore required to thwart enemies’ attempts to collaborate with data owners who have outsourced their data. In this paper, we propose a model for an effective data integrity and audit message verification system based on the ZSS (Zhang, Safavi, and Susilo) signature, which protects data privacy in cloud storage and uses faked data recovery to pay back original data using a modular approach. …”
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THE ETHICAL STARTUP? by Alexis Walker

“…Instead, the view proposed here would ask companies to do more to prevent or decrease the risk of a breach of privacy, unexpected later use of data, and reidentification. …”
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Consumer Protection in Cyberspace by Oscar H. Gandy, Jr.

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Comprehensive Review and Analysis of Cryptography Techniques in Cloud Computing by K. Sasikumar, Sivakumar Nagarajan

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Federated Learning for Heterogeneous Multi-Site Crop Disease Diagnosis by Wesley Chorney, Abdur Rahman, Yibin Wang, Haifeng Wang, Zhaohua Peng

“…The objective of this collaboration is to create a classifier that every farm can use to detect and manage rice crop diseases by leveraging data sharing while safeguarding data privacy. …”
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Ethereum blockchain for electronic health records: securing and streamlining patient management by J. S. Simi Mole, J. S. Simi Mole, R. S. Shaji, R. S. Shaji

“…Electronic health records (EHRs) are increasingly replacing traditional paper-based medical records due to their speed, security, and ability to eliminate redundant data. However, challenges such as EHR interoperability and privacy concerns remain unresolved. …”
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Convergence Analysis for Differentially Private Federated Averaging in Heterogeneous Settings by Yiwei Li, Shuai Wang, Qilong Wu

“…Federated learning (FL) has emerged as a prominent approach for distributed machine learning, enabling collaborative model training while preserving data privacy. However, the presence of non-i.i.d. data and the need for robust privacy protection introduce significant challenges in theoretically analyzing the performance of FL algorithms. …”
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A Systematic Literature Review of Machine Unlearning Techniques in Neural Networks by Ivanna Daniela Cevallos, Marco E. Benalcázar, Ángel Leonardo Valdivieso Caraguay, Jonathan A. Zea, Lorena Isabel Barona-López

“…This review examines the field of machine unlearning in neural networks, an area driven by data privacy regulations such as the General Data Protection Regulation and the California Consumer Privacy Act. …”
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DEBPIR: enhancing information privacy in decentralized business modeling by Gulshan Kumar, Rahul Saha, Mauro Conti, Tai Hoon Kim

“…Abstract Business modelling often involves extensive data collection and analysis, raising concerns about privacy infringement. …”
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A proxy for privacy uncovering the surveillance ecology of mobile apps by Signe Sophus Lai, Sofie Flensburg

“…We explore the surveillance ecology of mobile apps and thereby the privacy implications of everyday smartphone use through three analytical perspectives: The first focuses on the ‘appscapes’ of individual smartphone users and investigates the consequences of which and how many mobile apps users download on their phones; the second compares different types of apps in order to study the app ecology and the relationships between app and third-party service providers; and the third focuses on a particular app category and discusses the functional as well as the commercial incentives for permissions and third-party collaborations. …”
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Privacy computing:concept, connotation and its research trend by Feng-hua LI, Hui LI, Yan JIA, Neng-hai YU, Jian WENG

“…s: With the widespread application of mobile Internet, cloud computing and big data technologies, people enjoy the convenience of electronic business, information retrieval, social network and other services, whereas the threats of privacy leaks are ever growing due to the use of big data analytics. …”
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Providing Privacy Protection and Personalization Awareness for Android Devices by Hongliang Liang, Dongyang Wu, Shirun Liu, Hao Dai, Haifeng Liu

“…As a result, the risk of users compromising their privacy has risen exponentially. Mobile users currently cannot control how various applications handle the privacy of their sensor data. …”
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A Survey on Privacy-Preserving Machine Learning Inference by Stanisław Barański

“…Use cases in healthcare, finance, and education show how these techniques balance privacy with practical performance. …”
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Adaptive Transformation for Robust Privacy Protection in Video Surveillance by Mukesh Saini, Pradeep K. Atrey, Sharad Mehrotra, Mohan Kankanhalli

“…However, the current detectors are not fully reliable, leading to breaches in privacy protection. In this paper, we propose a privacy protection method that adopts adaptive data transformation involving the use of selective obfuscation and global operations to provide robust privacy even with unreliable detectors. …”
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Survey on vertical federated learning: algorithm, privacy and security by Jinyin CHEN, Rongchang LI, Guohan HUANG, Tao LIU, Haibin ZHENG, Yao CHENG

“…., model parameters, parameter gradients, embedding representation, etc.） applied to data distributed across various institutions.FL reduces the risk of privacy leakage, since raw data is not allowed to leave the institution.According to the difference in data distribution between institutions, FL is usually divided into horizontal federated learning （HFL）, vertical federated learning （VFL）, and federal transfer learning （TFL）.VFL is suitable for scenarios where institutions have the same sample space but different feature spaces and is widely used in fields such as medical diagnosis, financial and security of VFL.Although VFL performs well in real-world applications, it still faces many privacy and security challenges.To the best of our knowledge, no comprehensive survey has been conducted on privacy and security methods.The existing VFL was analyzed from four perspectives: the basic framework, communication mechanism, alignment mechanism, and label processing mechanism.Then the privacy and security risks faced by VFL and the related defense methods were introduced and analyzed.Additionally, the common data sets and indicators suitable for VFL and platform framework were presented.Considering the existing challenges and problems, the future direction and development trend of VFL were outlined, to provide a reference for the theoretical research of building an efficient, robust and safe VFL.…”
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