HoRNS-CNN model: an energy-efficient fully homomorphic residue number system convolutional neural network model for privacy-preserving classification of dyslexia neural-biomarkers by Opeyemi Lateef Usman, Ravie Chandren Muniyandi, Khairuddin Omar, Mazlyfarina Mohamad, Ayoade Akeem Owoade, Morufat Adebola Kareem

“…Although, fully homomorphic encryption (FHE)-based methods have been proposed to maintain data confidentiality and privacy, however, existing FHE deep convolutional neural network (CNN) models still face some issues such as low accuracy, high encryption/decryption latency, energy inefficiency, long feature extraction times, and significant cipher-image expansion. …”
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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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Fused federated learning framework for secure and decentralized patient monitoring in healthcare 5.0 using IoMT by Bassam Almogadwy, Abdulrahman Alqarafi

“…Abstract Federated Learning (FL) enables artificial intelligence frameworks to train on private information without compromising privacy, which is especially useful in the medical and healthcare industries where the knowledge or data at hand is never enough. …”
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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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Neuroethical issues in adopting brain imaging for personalized chronic pain management: Attitudes of people with lived experience of chronic pain by Karen Deborah Davis, Monica de Oliveira, Ariana Besik, Daniel Z. Buchman

“…However, they worried that brain scans could be used to dismiss their pain self-report. Most respondents felt there were policies to protect their brain data, but 40% were concerned about privacy and brain scan use against them by their employers/insurers. …”
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Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks by Lijing Ren, Denghui Zhang

“…This prevalence poses challenges for secure image forwarding, as it is susceptible to privacy leaks when sharing data. While standard encryption algorithms can safeguard the privacy of textual data, image data entail larger volumes and significant redundancy. …”
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Phishing as a form of fraud identity theft in healthcare: Victimization during COVID-19 pandemic by Vilić Vida

“…The positive aspects of this kind of communication are undoubtedly obvious and necessary, but they also created new forms of victimization due to insecure protocols for sending e-mails, inadequate level of privacy protection, insufficient information security, the existence of so-called security holes and the use of the same devices and digital services for professional and private purposes. …”
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A technological and legal investigation into how smart states deploy collective intelligence for security and surveillance purposes by Diana M. POPA

“…The article debates the relation between data protection and public and national security in democratic states and shows how the evolving threat landscape influences both the practice and the legislative process around personal data protection and deployment of emerging technologies and use of collective intelligence for security purposes. …”
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Optimizing Industrial IoT Data Security Through Blockchain-Enabled Incentive-Driven Game Theoretic Approach for Data Sharing by Muhammad Noman Sohail, Adeel Anjum, Iftikhar Ahmed Saeed, Madiha Haider Syed, Axel Jantsch, Semeen Rehman

“…However, in the Industrial Internet of Things (IIoT), the sharing of data has bandwidth, computational, and privacy issues. …”
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Decades in the Making: The Evolution of Digital Health Research Infrastructure Through Synthetic Data, Common Data Models, and Federated Learning by Jodie A Austin, Elton H Lobo, Mahnaz Samadbeik, Teyl Engstrom, Reji Philip, Jason D Pole, Clair M Sullivan

“…The same way that drug trials require infrastructure to support their conduct, digital health also necessitates new and disruptive research data infrastructure. Novel methods such as common data models, federated learning, and synthetic data generation are emerging to enhance the utility of research using RWD, which are often siloed across health systems. …”
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Normative framework for the protection against genetic discrimination in Serbia by Petrušić Nevena

“…The analysis has shown that the rules on protection of the genetic data and the use of genetic samples, which are crucially important for prevention of genetic discrimination, have not been harmonized with contemporary ethical and legal standards. …”
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Covert surveillance of communications: Compliance with the practice of the European court of human rights by Kovačević Milica

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A secure and efficient user selection scheme in vehicular crowdsensing by Min Zhang, Qing Ye, Zhimin Yuan, Kaihuan Deng

“…At present, user selection schemes have the following lacks: (1) Privacy protection and data security are often neglected, resulting in reduced user participation willingness. (2) Insufficient data quality assurance and challenges in processing high-dimensional redundant data remain significant issues. (3) Key exchange protocols rely on traditional cryptographic algorithms, which struggle to comply with Chinese cryptographic standards and incur high overhead. …”
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BANK INDONESIA’S ROLE IN MITIGATING ISSUES OF MONETARY ECONOMIC SOVEREIGNTY AND HUMAN RIGHTS by Dara Salsabila

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Efficient secure federated learning aggregation framework based on homomorphic encryption by Shengxing YU, Zhong CHEN

“…In order to solve the problems of data security and communication overhead in federated learning, an efficient and secure federated aggregation framework based on homomorphic encryption was proposed.In the process of federated learning, the privacy and security issues of user data need to be solved urgently.However, the computational cost and communication overhead caused by the encryption scheme would affect the training efficiency.Firstly, in the case of protecting data security and ensuring training efficiency, the Top-K gradient selection method was used to screen model gradients, reducing the number of gradients that need to be uploaded.A candidate quantization protocol suitable for multi-edge terminals and a secure candidate index merging algorithm were proposed to further reduce communication overhead and accelerate homomorphic encryption calculations.Secondly, since model parameters of each layer of neural networks had characteristics of the Gaussian distribution, the selected model gradients were clipped and quantized, and the gradient unsigned quantization protocol was adopted to speed up the homomorphic encryption calculation.Finally, the experimental results show that in the federated learning scenario, the proposed framework can protect data privacy, and has high accuracy and efficient performance.…”
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New cross-layer reputation mechanism for mobile cloud computing by Mengyang YU, Hui LIN, Youliang TIAN

“…Users are facing increasingly serious security threats such as data leakage and privacy exposure while using various mobile cloud services.Based on the data security and privacy protection for mobile cloud computing research background,combining the cross-layer design and credit mechanism,in the node between the introduction of the MAC layer and network layer in the process of credit evaluation of factors affecting user reputation,to identify and manage internal malicious nodes.The credibility of the simulation results show that the proposed mechanism can effectively resist the internal defamation and multilayer attack,enhance the credibility of mobile terminals,thus improve the mobile cloud service data security and privacy protection.…”
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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

“…Our architecture minimizes privacy risks by ensuring that raw data never leaves the device; only model updates are shared for aggregation at the global level. …”
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WiFi-Based Location Tracking: A Still Open Door on Laptops by Mariana Cunha, Ricardo Mendes, Yves-Alexandre de Montjoye, Joao P. Vilela

“…Location privacy is a major concern in the current digital society, due to the sensitive information that can be inferred from location data. …”
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KAB: A new k-anonymity approach based on black hole algorithm by Lynda Kacha, Abdelhafid Zitouni, Mahieddine Djoudi

“…K-anonymity is the most widely used approach to privacy preserving microdata which is mainly based on generalization. …”
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