Enhancing Data Security in Distributed Systems Using Homomorphic Encryption and Secure Computation Techniques by Parihar Bhawana, Kiran Ajmeera, Valaboju Sabitha, Rashid Syed Zahidur, Liyakat Kazi Kutubuddin Sayyad, D R Anita Sofia Liz

“…Traditional solutions tend to use standard techniques like basic data wrapping and cryptographic 'rings'; but, due to the design properties required, they end up as lightweight mechanisms, usually not interpretation-at-all capable because of the need for protecting data during processing - leaving these applications hard to use and maintain long-term, or otherwise, limited to cloud computing and federated learning, when individual data types can be worked on within providers like AWS, Azure, etc, etc; or, even, explaining the results with near total indifference to the underlying big data tools, analytics, or neural architectures. …”
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Proposed ethical framework for software requirements engineering by Seblewongel E. Biable, Nuno M. Garcia, Dida Midekso

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Advanced Zero-Shot Learning (AZSL) Framework for Secure Model Generalization in Federated Learning by Muhammad Asif, Surayya Naz, Faheem Ali, Amerah Alabrah, Abdu Salam, Farhan Amin, Faizan Ullah

“…The objective of the proposed model is to increase model generalizability while achieving better performance on various and heterogeneous datasets while preserving data privacy. We have used datasets named MNIST, CIFAR-10, and FEMNIST to measure the model’s accuracy, precision, and generalization capability. …”
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My privacy at risk – my guard is on: a study of SNS use among young adults by Meenakshi Handa, Ronika Bhalla, Parul Ahuja

“…Purpose – Increasing incidents of privacy invasion on social networking sites (SNS) are intensifying the concerns among stakeholders about the misuse of personal data. …”
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Solving blockchain trilemma using off‐chain storage protocol by Saha Reno, Md. Mokammel Haque

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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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Consumer Protection in Cyberspace by Oscar H. Gandy, Jr.

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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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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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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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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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Secondary Use of Electronic Health Record: Opportunities and Challenges by Shahid Munir Shah, Rizwan Ahmed Khan

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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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Balancing Ethics and Privacy in the Use of Artificial Intelligence in Institutions of Higher Learning: A Framework for Responsive AI Systems by Belinda Ndlovu, Kudakwashe Maguraushe

“…Thematic analysis identified ten critical themes centred around benefits, challenges, applications, responsible use, privacy and data security, ethical considerations, institutional policies and frameworks, training, equity, and sustainable AI use. …”
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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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