Time‐specific encrypted range query with minimum leakage disclosure by Ozgur Oksuz

“…Abstract A time‐specific encrypted range query scheme that has the following properties is proposed. (1) The proposed scheme has trapdoor privacy and data privacy so that a semi‐honest cloud is not able to get any useful information from given ciphertexts and given tokens that are used for searching ranges. (2) Unlike most of the other studies which report that the cloud server stores single encrypted keyword/element in the database, in our solution, the cloud server stores encrypted multi‐keywords/ranges in the database. …”
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AI-based treatment of psychological conditions: the potential use, benefits and drawbacks by Michael Baber, Barbara Baker

“…This suggests that translating small-scale, short-term trials into effective large-scale, longer-term real-world applications may be a particular challenge. While the use of AI in mental healthcare appears to have potential, its use also raises important ethical and privacy concerns, potential risk of bias, and the risk of unintended consequences such as over-diagnosis or unnecessary treatment of normal emotional experiences. …”
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Physically secure and fog-enabled lightweight authentication scheme for WBAN by Jegadeesan Subramani, Arun Sekar Rajasekaran, Arunkumar Balakrishnan, G. Anantha Rao

“…Abstract Wireless Body Area Networks (WBANs) are vital for healthcare, fitness monitoring, and remote patient care by means of combining sensors and wearable technologies for data collection and transmission. However, ensuring secure communication in WBANs remains a critical challenge and is generally insecure against the manipulation of data, breaches of privacy, and unauthorized access. …”
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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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Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection by Amogh Deshmukh, Peplluis Esteva de la Rosa, Raul Villamarin Rodriguez, Sandeep Dasari

“…To address these issues, federated learning (FL) using a flower framework is utilized to protect the privacy of individual organizations while still collaborating through separate models to create a unified global model. …”
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Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing by Nada Alasbali, Jawad Ahmad, Ali Akbar Siddique, Oumaima Saidani, Alanoud Al Mazroa, Asif Raza, Rahmat Ullah, Muhammad Shahbaz Khan

“…Most existing automated detection/classification approaches that utilize machine learning or deep learning poses privacy issues, as they involve centralized computing and require local storage for data training.MethodsKeeping the privacy of sensitive patient data as a primary objective, in addition to ensuring accuracy and efficiency, this paper presents an algorithm that integrates Federated learning techniques into an IoT-based edge-computing environment. …”
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A Collaborative Framework for Privacy Preserving Fuzzy Co-Clustering of Vertically Distributed Cooccurrence Matrices by Katsuhiro Honda, Toshiya Oda, Daiji Tanaka, Akira Notsu

“…In many real world data analysis tasks, it is expected that we can get much more useful knowledge by utilizing multiple databases stored in different organizations, such as cooperation groups, state organs, and allied countries. …”
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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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Explainable Federated Framework for Enhanced Security and Privacy in Connected Vehicles Against Advanced Persistent Threats by Sudhina Kumar G K, Krishna Prakasha K, Balachandra Muniyal, Muttukrishnan Rajarajan

“…The critical need for vehicular data privacy restricts traditional centralized Machine Learning (ML) approaches. …”
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An Easily Scalable Docker-Based Privacy-Preserving Malicious Traffic Detection Architecture for IoT Environments by Tong Niu, Yaqiu Liu, Qingfeng Li, Qichi Bao

“…The model is then trained using federated learning/edge computing to ensure data privacy. …”
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Privacy-Preserving U-Net Variants with pseudo-labeling for radiolucent lesion segmentation in dental CBCT by Amelia Ritahani Ismail, Faris Farhan Azlan, Khairul Akmal Noormaizan, Nurul Afiqa, Syed Qamrun Nisa, Ahmad Badaruddin Ghazali, Andri Pranolo, Shoffan Saifullah

“…To safeguard sensitive information, Differential Privacy Stochastic Gradient Descent (DP-SGD) is integrated using TensorFlow-Privacy, achieving a privacy budget of ε ≈ 1.5 with minimal performance degradation. …”
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The Use of AI in Elementary School Learning: A Systematic Literature Review by Natalina Purba, Desti Pujiati, Partohap Saut Raja Sihombing, Hendra Simanjuntak, Desi Sijabat

“…However, the study also highlights challenges such as inadequate infrastructure, teacher competence, and ethical concerns regarding data privacy and algorithmic bias. Addressing these challenges requires investment in infrastructure, ongoing teacher training, and clear ethical guidelines to ensure equitable access to AI-driven learning. …”
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A Blockchain-Based Privacy Protection Model Under Quality Consideration in Spatial Crowdsourcing Platforms by Amal Albilali, Maysoon Abulkhair, Manal Bayousef, Faisal Albalwy

“…This innovative model combines the strengths of centralized efficiency and decentralized privacy, and introduces a unique mechanism that significantly enhances privacy protection and ensures data integrity. …”
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A Comparative Study of Privacy-Preserving Techniques in Federated Learning: A Performance and Security Analysis by Eman Shalabi, Walid Khedr, Ehab Rushdy, Ahmad Salah

“…Federated learning (FL) is a machine learning technique where clients exchange only local model updates with a central server that combines them to create a global model after local training. While FL offers privacy benefits through local training, privacy-preserving strategies are needed since model updates can leak training data information due to various attacks. …”
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Perceived risk and intention to use digital financial services of Vietnamese youth by Nguyễn Thị Thương, Khúc Thế Anh, Nguyễn Mạnh Cường, Phan Anh Tuấn, Bùi Thị Thanh Huyền, Võ Mỹ Linh

“…SPSS26 and AMOS24 software were used for data processing. The results show that “Security risk” does not affect the “Perceived Risk” of the youth. …”
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Opportunities and Challenges: The Use of ChatGPT in Enabling Library Knowledge Services by Huaming LI

“…At the same time, the application focuses on the risks and challenges posed by technical limitations, intellectual property rights, user privacy, harmful information, data sources, academic integrity and other aspects. …”
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Differential Private POI Queries via Johnson-Lindenstrauss Transform by Mengmeng Yang, Tianqing Zhu, Bo Liu, Yang Xiang, Wanlei Zhou

“…In addition, the proposed perturbation method based on the Johnson Lindenstrauss transform satisfies the differential privacy. Two popular point of interest queries, <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-NN and Range, are used to evaluate the method on two real-world data sets. …”
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COVID-19 detection using federated machine learning. by Mustafa Abdul Salam, Sanaa Taha, Mohamed Ramadan

“…AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. …”
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Behavioural Economics and Consumer Decision-Making in the Age of Artificial Intelligence (AI), Data Science, Business Analytics, and Internet of Things (IoT) by Yuki Haruto Yamamoto

“…However, while AI and IoT facilitate more efficient and engaging consumer experiences, they introduce complex ethical and practical concerns, particularly around data privacy, algorithmic fairness, and maintenance of consumer trust. …”
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Using Federated Learning for Collaborative Intrusion Detection Systems by Matteo Rizzato, Youssef Laarouchi, Christophe Geissler

“…Traditional implementations provide fast and accurate predictions, but require centralised storage of labelled historical data for training. This solution is not always suitable for real-world applications, where regulatory constraints and privacy concerns hamper the collection of sensitive data into a single server. …”
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