Enhancing Privacy in IoT Networks: A Comparative Analysis of Classification and Defense Methods by Ahmet Emre Ergun, Ozgu Can, Murat Kantarcioglu

“…Therefore, the study examines privacy risks associated with sequential IoT device data and evaluates the effectiveness of ML algorithms using two datasets. …”
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CropsDisNet: An AI-Based Platform for Disease Detection and Advancing On-Farm Privacy Solutions by Mohammad Badhruddouza Khan, Salwa Tamkin, Jinat Ara, Mobashwer Alam, Hanif Bhuiyan

“…The integration of a differential privacy algorithm into our CropsDisNet model could establish the benefits of automated crop disease classification without compromising on-farm data privacy by reducing training data leakage. …”
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Secure distributed data integrity auditing with high efficiency in 5G-enabled software-defined edge computing by Dengzhi Liu, Zhimin Li, Dongbao Jia

“…The proposed auditing scheme in this paper can be used to guarantee the correctness and the completeness of the stored data in 5G-enabled software-defined edge computing. …”
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Security, QoS and energy aware optimization of cloud-edge data centers using game theory and homomorphic encryption: Modeling and formal verification by Mbarek Marwan, Abdelkarim Ait Temghart, Said Ouhmi, Mohamed Lazaar

“…First of all, we develop a secure offloading technique that can process data while preserving privacy. Although RSA and Paillier cryptosystems are highly beneficial for mathematical operations on basic encrypted data due to their homomorphic properties, images are processed using permutation-ordered binary (POB) and Shamir's secret sharing (SSS). …”
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Ethical Considerations in Emotion Recognition Research by Darlene Barker, Mukesh Kumar Reddy Tippireddy, Ali Farhan, Bilal Ahmed

“…The framework introduces structural safeguards which include data minimization, adaptive consent mechanisms, and transparent model logic as a more complete solution than privacy or fairness approaches. …”
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Factors influencing patients’ willingness to share their digital health data for primary and secondary use: A theory- and evidence-based overview of reviews by Sabrina Fesl, Caroline Lang, Jochen Schmitt, Stefanie Brückner, Stephen Gilbert, Stefanie Deckert, Madlen Scheibe

“…SRs underwent a multistage screening process using the inclusion and exclusion criteria based on the Population, Concept/Construct, and Context (PCC) framework, followed by data extraction and quality assessment using revised measurement tool to assess systematic reviews (R-AMSTAR2). …”
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Estimating rare disease prevalence and costs in the USA: a cohort study approach using the Healthcare Cost Institute claims data by Keith A Crandall, Christine M Cutillo, Ainslie Tisdale, Mahdi Baghbanzadeh, Reva L Stidd, Manpreet S Khural, Laurie J Hartman, Jeff Greenberg, Kevin B Zhang, Ali Rahnavard

“…Building capabilities to use machine learning to accelerate the diagnosis of RDs would vastly improve with changes to healthcare data, such as standardising data input, linking databases, addressing privacy issues and assigning ICD-10 codes for all RDs, resulting in more robust data for RD analytics.…”
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Patient and clinician opinions on internet search data use in therapy: Insights, considerations, and guidelines for integrating a new digital phenotyping measure by Alex Dhima, Soumya Choudhary, Keris Myrick, John Torous

“…More granular search data in visualizations was considered more useful when compared to vague search frequencies. …”
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Robust Synthetic Data Generation for Sequential Financial Models Using Hybrid Variational Autoencoder–Markov Chain Monte Carlo Architectures by Francesco Bruni Prenestino, Enrico Barbierato, Alice Gatti

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Lessons Learned From European Health Data Projects With Cancer Use Cases: Implementation of Health Standards and Internet of Things Semantic Interoperability by Amelie Gyrard, Somayeh Abedian, Philip Gribbon, George Manias, Rick van Nuland, Kurt Zatloukal, Irina Emilia Nicolae, Gabriel Danciu, Septimiu Nechifor, Luis Marti-Bonmati, Pedro Mallol, Stefano Dalmiani, Serge Autexier, Mario Jendrossek, Ioannis Avramidis, Eva Garcia Alvarez, Petr Holub, Ignacio Blanquer, Anna Boden, Rada Hussein

“…These initiatives aim to enhance the analysis of heterogeneous health data while aligning with EHDS implementation, specifically for the EHDS for the secondary use of data (EHDS2). …”
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Concurrent prescriptions for opioids and benzodiazepines and risk of opioid overdose: protocol for a retrospective cohort study using linked administrative data by Kristian B Filion, Robyn Tamblyn, David L Buckeridge, Erin Y Liu

“…Our aim was to assess the risk of opioid overdose associated with concurrent use of opioids and benzodiazepines relative to opioids alone.Methods and analysis A retrospective cohort study will be conducted using medical claims data from adult residents of Montréal, Canada. …”
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PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation by Zhengqiang Ge, Xinyu Liu, Qiang Li, Yu Li, Dong Guo

“…To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. …”
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A Systematic-Narrative Review of Online Proctoring Systems and a Case for Open Standards by Eva Heinrich

“…However, concerns about data privacy and security, transparency in data processing and decision-making, and the ethical use of online proctoring systems have been raised. …”
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An Efficient Elliptic Curve Cryptography-Based Secure Communication with Privacy Preserving for Autonomous Vehicle by Jiongen Xiao, Yi Liu, Yi Zou, Dacheng Li, Tao Leng

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FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification by Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal, Bhavani Sankar Panda, Hardik Doshi, Nafeesh Ahmad, Vivek Bhardwaj, Gaurav Sharma

“…Then implement the federated learning (FL) framework with IID (Independent and identically distributed) and Non-IID datasets maintain similar results by preserving data. Further FL-QNNs approach is used with these baseline models and achieved 98-99% accuracy and while optimizing memory, preserving data and resources. …”
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A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and Beyond by Ahmad Rahdari, Elham Keshavarz, Ehsan Nowroozi, Rahim Taheri, Mehrdad Hajizadeh, Mohammadreza Mohammadi, Sima Sinaei, Thomas Bauschert

“…Particular attention is given to progress in federated learning, which trains a model across multiple devices without sharing the actual data, keeping data private and secure. The survey also highlights federated learning techniques, including secure federated learning, by detecting malicious updates and privacy-preserving federated learning via data encryption, data perturbation, and anonymization, as new paradigms for building responsible computing systems. …”
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Privacy Policy Pop-up: A Genre Analysis of Journal Websites’ HTTP Cookies by Lisbeth Sinan Lendik

“… Online users are made to believe that they have control over their data privacy through the selection of website cookie options. …”
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Secure and Lightweight Cipher for Resource-constrained IoT Healthcare Applications using Snake Key Generation by Saja Kareem Ismael, Mohamed Ibrahim Shujaa, Ahmed Bahaaulddin A. Alwahhab

“…Furthermore, this study examines the double-layer encryption architecture used to secure data in at rest and during transmission from a sensor’s gathering location to long-term storage. …”
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A Telehealth Privacy and Security Self-Assessment Questionnaire for Telehealth Providers: Development and Validation by Leming Zhou, Robert Thieret, Valerie Watzlaf, Dilhari DeAlmeida, Bambang Parmanto

“…It is necessary for telehealth care providers to demonstrate that they have taken sufficient actions to protect their patients’ data security and privacy. The government provided a HIPAA audit protocol that is highly useful for internal security and privacy auditing on health care systems, however, this protocol includes extensive details that are not always specific to telehealth and therefore is difficult to be used by telehealth practitioners. …”
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Artificial Intelligence–Enabled Facial Privacy Protection for Ocular Diagnosis: Development and Validation Study by Haizhu Tan, Hongyu Chen, Zhenmao Wang, Mingguang He, Chiyu Wei, Lei Sun, Xueqin Wang, Danli Shi, Chengcheng Huang, Anping Guo

“… Abstract BackgroundFacial biometric data, while valuable for clinical applications, poses substantial privacy and security risks. …”
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