A collaborative data storage with incentive mechanism for blockchain-based IoV by Quan Shi, Lankai Wang, Chen Chen

“…As the volume of data in the Internet of Vehicles (IoV) continues to grow, challenges such as insufficient storage capacity and potential privacy breaches become more pronounced. …”
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Highly Secure and Adaptive Multisecret Sharing for Reversible Data Hiding in Encrypted Images by Jiang-Yi Lin, Ching-Chun Chang, Chin-Chen Chang, Chin-Feng Lee

“…Reversible data hiding in encrypted images (RDHEI) is a technique that not only allows the cover images can be fully restored without any loss of information after the embedded data has been extracted but also ensures the confidentiality within the cover images. …”
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Bibliometric mapping of data science in education: Trends, benefits, challenges, and future directions by Muhammad Aizri Fadillah, Syafrijon, Sulandari, Febry Azmiana Siregar, Usmeldi

“…Co-occurrence analysis reveals three main clusters: DS approaches, student interactions with learning, and data analysis techniques. This study identifies key research trends and challenges, including limited infrastructure, skills gaps among educators, and privacy concerns, while addressing gaps in the literature regarding the long-term impact and contextualization of DS in education. …”
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A decentralised architecture for secure exchange of assets in data spaces: The case of SEDIMARKGitHub by Erika Duriakova, Diarmuid O’Reilly-Morgan, Maroua Bahri, Maxime Costalonga, Gabriel Danciu, Septimiu Nechifor, Stefan-Cristian Jarcau, Tarek Elsaleh, Peipei Wu, Franck Le Gall, Grigorios Koutantos, Panagiotis Vlacheas, Luis Sánchez, Juan Ramón Santana, Pablo Sotres, Elias Tragos

“…Together the technologies used in SEDIMARK ensure privacy, trust and data quality for secure management, sharing, and monetisation of assets in data spaces.…”
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Mitigating Malicious Insider Threats to Common Data Environments in the Architecture, Engineering, and Construction Industry: An Incomplete Information Game Approach by KC Lalropuia, Sanjeev Goyal, Borja García de Soto, Dongchi Yao, Muammer Semih Sonkor

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Legal Reform of Artificial Intelligence's Liability to Personal Data Perspectives of Progressive Legal Theory by Junaidi Junaidi, Pujiono Pujiono, Rozlinda Mohamed Fadzil

“…The research found that the use of Artificial Intelligence in collecting and analysing personal data can threaten individual privacy. …”
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Attitudes and preferences toward the adoption of voice-controlled intelligent personal assistants: Evidence from Croatia by Antun Biloš, Bruno Budimir, Borna Kraljević

“…This paper provides an examination of the differences between IPA users and non-users as well as an overview of (non-)user perception related to privacy and security issues associated with IPA and trust in companies' appropriate use of IPA-generated data. …”
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A DLP Module Design Based on Plug-in for MS Word by Hussein Al-sanabani, Murat İskefiyeli

“…This model uses security measures such as data encryption and access control to prevent accidental leakage of the classified files by the insiders. …”
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COMPUTATIONAL THINKING IN THE PROBLEM OF RELIABLE AND SECURE DATA TRANSMISSION OVER A COMMUNICATION CHANNEL by Kirill V. Agievets, Andarbek Z. Ahmadov, Grigory A. Ivanenko, Mikhail A. Ivanov, Mikael A. Kondakhchan, Andrey V. Starikovsky

“…There are three problems that have to be solved when transmitting data over communication channels: detection and correction of errors caused by interference in communication channels; ensuring of transmitted data privacy; protection against injection of false data. …”
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Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis by Md. Redwan Ahmed, Hamdadur Rahman, Zishad Hossain Limon, Md Ismail Hossain Siddiqui, Mahbub Alam Khan, Al Shahriar Uddin Khondakar Pranta, Rezaul Haque, S M Masfequier Rahman Swapno, Young-Im Cho, Mohamed S. Abdallah

“…However, deep learning (DL) models used in healthcare face significant challenges, including concerns about data privacy, domain-specific overfitting, and limited interpretability. …”
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Traffic concealed data detection method based on contrastive learning and pre-trained Transformer by HE Shuai, ZHANG Jingchao, XU Di, JIANG Shuai, GUO Xiaowei, FU Cai

“…To solve the problems of characterizing representing massive encrypted traffic, perceiving malicious behaviors, and identifying the ownership of privacy data, a traffic concealed data detection method was proposed based on contrastive learning and pre-trained Transformer. …”
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Development of a large Danish single-centre retro- and prospective transgender cohort using a web-based platform: a study protocol of a transgender cohort study by Ulrik Schiøler Kesmodel, Allan Stensballe, Emma Elisabeth Skovby Petersen, Frederika Kiy, Michael Winterdahl, Jakob Dal, Maria Lucia Pop, Gry Kjaersdam Telléus, Astrid Ditte Højgaard

“…Medical records will provide information on demographics, mental health, physical health, and gender-affirming treatment details. Data will be managed using REDCap, ensuring compliance with GDPR and the National Data Protection Act.Ethics and dissemination While recognising the potential privacy risks associated with data collection, the study considers these outweighed by the benefits of advancing knowledge on gender diversity and the impacts of gender-affirming care. …”
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Data-Centric Fine-Tuning of Small Language Models for Automatic Extraction of Technical Requirements by Leopold Muller, Nina Schwarz, Lars Bocking, Andreas Bereczuk, Hanno Stagge, Wolfgang Kratsch, Niklas Kuhl

“…These results underscore the potential of data-centric fine-tuning for adapting small language models to high-stakes, privacy-sensitive tasks, offering a scalable alternative to LLMs enabling their applications to real-world use cases.…”
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Reversible data hiding algorithm in encrypted images based on prediction error and bitplane coding by Haiyong WANG, Mengning JI

“…With the increasing use of cloud backup methods for storing important files, the demand for privacy protection has also grown.Reversible data hiding in encrypted images (RDHEI) is an important technology in the field of information security that allows embedding secret information in encrypted images while ensuring error-free extraction of the secret information and lossless recovery of the original plaintext image.This technology not only enhances image security but also enables efficient transmission of sensitive information over networks.Its application in cloud environments for user privacy protection has attracted significant attention from researchers.A reversible data hiding method in encrypted images based on prediction error and bitplane coding was proposed to improve the embedding rate of existing RDHEI algorithms.Different encoding methods were employed by the algorithm depending on the distribution of the bitplanes, resulting in the creation of additional space in the image.The image was rearranged to allocate the freed-up space to the lower-order planes.Following this, a random matrix was generated using a key to encrypt the image, ensuring image security.Finally, the information was embedded into the reserved space.The information can be extracted and the image recovered by the receiver using different keys.The proposed algorithm achieves a higher embedding rate compared to five state-of-the-art RDHEI algorithms.The average embedding rates on BOWS-2, BOSSBase, and UCID datasets are 3.769 bit/pixel, 3.874 bit/pixel, and 3.148 bit/pixel respectively, which represent an improvement of 12.5%, 6.9% and 8.6% compared to the best-performing algorithms in the same category.Experimental results demonstrate that the proposed algorithm effectively utilizes the redundancy of images and significantly improves the embedding rate.…”
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Pursuit of Digital Innovation in Psychiatric Data Handling Practices in Ireland: Comprehensive Case Study by Rana Zeeshan, John Bogue, Amna Gill, Mamoona Naveed Asghar

“…Our survey reported an average score of 4.37 of 10 (SD 1.25) given by participants in terms of technology use. Regarding privacy measures, 75% (n=12) of participants mentioned that staff members are allowed to keep their phones while accessing psychiatric case notes. …”
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Optimising Data Analytics to Enhance Postgraduate Student Academic Achievement: A Systematic Review by Mthokozisi Masumbika Ncube, Patrick Ngulube

“…As such, this review aimed to identify data analytics approaches used by HEIs and explore challenges and best practices in their application. …”
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Blockchain-enabled federated learning with edge analytics for secure and efficient electronic health records management by Munusamy S, Jothi K R

“…Abstract The rapid adoption of Federated Learning (FL) in privacy-sensitive domains such as healthcare, IoT, and smart cities underscores its potential to enable collaborative machine learning without compromising data ownership. …”
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Federated learning with tensor networks: a quantum AI framework for healthcare by Amandeep Singh Bhatia, David E Bernal Neira

“…The healthcare industry frequently handles sensitive and proprietary data, and due to strict privacy regulations, it is often reluctant to share it directly. …”
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Ciphertext policy hidden access control scheme based on blockchain and supporting data sharing by Ruizhong DU, Tianhe ZHANG, Pengliang SHI

“…Objectives: Although the traditional attribute-based encryption scheme achieves one-to-many access control,there are still challenges such as single point of failure,low efficiency,no support for data sharing,and privacy leakage.To solve these problems,a ciphertext policy hidden access control scheme based on blockchain and supporting data sharing is proposed. …”
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Leveraging data analytics for detection and impact evaluation of fake news and deepfakes in social networks by Tony Mathew Abraham, Tao Wen, Ting Wu, Yu-wang Chen

“…The rampant rise of deepfake technology poses huge risks to social trust and privacy issues, which impacts both individuals and society at large, and leveraging the effective use of data analytics, machine learning and AI techniques can help prevent irreparable damage and mitigate the negative impacts of deepfakes in social networks. …”
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