Navigating Privacy: A Global Comparative Analysis of Data Protection Laws by Sungjin Lim, Junhyoung Oh

“…By identifying specific limitations and areas for improvement in each region’s data protection laws, this study contributes to the ongoing discourse on international data privacy regulation. It offers valuable insights for policymakers and stakeholders seeking to navigate the complexities of the data economy while ensuring robust safeguards for individual privacy.…”
Get full text

A scoping review by I Gusti Ayu Tirtayani, I Made Wardana, Putu Yudi Setiawan, I Gst. Ngr. Jaya Agung Widagda K

“…However, challenges such as data privacy concerns, algorithmic biases, and the need for greater transparency are also noted. …”
Get full text

Market Trends in 2024 in the IT Project Management Industry by Nicoleta-Madalina Stan, Adrian Bogorin Predescu, Aurel Mihail Titu

“…The increasing importance of cybersecurity and data privacy is influencing project management practices, with a heightened focus on risk management and compliance. …”
Get full text

Contrastive Dual-Pool Feature Adaption for Domain Incremental Remote Sensing Scene Classification by Yingzhao Shao, Yunsong Li, Xiaodong Han

“…Existing solutions, including rehearsal-based and prompt-based methods, face limitations such as data privacy concerns, high computational overhead, and unreliable feature embeddings due to domain gaps. …”
Get full text

The Data Heterogeneity Issue Regarding COVID-19 Lung Imaging in Federated Learning: An Experimental Study by Fatimah Alhafiz, Abdullah Basuhail

“…Federated learning (FL) has emerged as a transformative framework for collaborative learning, offering robust model training across institutions while ensuring data privacy. In the context of making a COVID-19 diagnosis using lung imaging, FL enables institutions to collaboratively train a global model without sharing sensitive patient data. …”
Get full text

Privacy leakage risk assessment for reversible neural network by Yifan HE, Jie ZHANG, Weiming ZHANG, Nenghai YU

“…In recent years, deep learning has emerged as a crucial technology in various fields.However, the training process of deep learning models often requires a substantial amount of data, which may contain private and sensitive information such as personal identities and financial or medical details.Consequently, research on the privacy risk associated with artificial intelligence models has garnered significant attention in academia.However, privacy research in deep learning models has mainly focused on traditional neural networks, with limited exploration of emerging networks like reversible networks.Reversible neural networks have a distinct structure where the upper information input can be directly obtained from the lower output.Intuitively, this structure retains more information about the training data, potentially resulting in a higher risk of privacy leakage compared to traditional networks.Therefore, the privacy of reversible networks was discussed from two aspects: data privacy leakage and model function privacy leakage.The risk assessment strategy was applied to reversible networks.Two classical reversible networks were selected, namely RevNet and i-RevNet.And four attack methods were used accordingly, namely membership inference attack, model inversion attack, attribute inference attack, and model extraction attack, to analyze privacy leakage.The experimental results demonstrate that reversible networks exhibit more serious privacy risks than traditional neural networks when subjected to membership inference attacks, model inversion attacks, and attribute inference attacks.And reversible networks have similar privacy risks to traditional neural networks when subjected to model extraction attack.Considering the increasing popularity of reversible neural networks in various tasks, including those involving sensitive data, it becomes imperative to address these privacy risks.Based on the analysis of the experimental results, potential solutions were proposed which can be applied to the development of reversible networks in the future.…”
Get full text

CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism by Chongzhen Zhang, Hongye Sun, Zhaoyu Shen, Dongyu Wang

“…Federated learning enables multiple intelligent devices to collaboratively perform machine learning tasks while preserving local data privacy. However, traditional FL architectures face challenges such as centralization and lack of effective defense mechanisms against malicious nodes, particularly in large-scale edge computing scenarios, which can lead to system instability. …”
Get full text

The present and future of digital health, digital medicine, and digital therapeutics for allergic diseases by He Zhang, Yang Cao, Haibo Jiang, Qilin Zhou, Qintai Yang, Lei Cheng

“…Challenges such as data privacy, interoperability, and equitable access are addressed, alongside potential strategies to overcome these barriers. …”
Get full text

Energy Efficiency of Kernel and User Space Level VPN Solutions in AIoT Networks by ALEKSANDAR JEVREMOVIC, Zona Kostic, Ivan Chorbev, Dragan Perakovic, Andrii Shalaginov, Ivan Cvitic

“…Numerous factors contribute to this trend, including the requirement for immediate response, the need to protect data privacy/security, a lack of adequate infrastructure, and the desire to reduce costs. …”
Get full text

Harnessing Artificial Intelligence for ESL Assessments: Efficiency, Challenges, and Future Directions by Seyed Reza Abedi, Farnaz Divanpour, Seyed Reza Molaee, Hailay Tesfay Gebremariam

“…Qualitative insights from 20 instructors reveal challenges, including algorithmic bias, cultural insensitivity, and concerns over data privacy. Despite these issues, AI tools are praised for reducing grading time and providing instant feedback. …”
Get full text

Application of edge computing technology in smart grid data security by Zhuo Cheng, Jiangxin Li, Jianjun Zhang, Chen Wang, Hui Wang, Juyin Wu

“…By blinding the power and information, the signcrypter can not know the specific power consumption information of the user, so as to ensure the data privacy and security of the user. Implement forward security using proxy key update mechanism and perform batch verification of user signature ciphertext. …”
Get full text

Comprehensive Review of Privacy, Utility, and Fairness Offered by Synthetic Data by A. Kiran, P. Rubini, S. Saravana Kumar

“…We understand how data privacy, utility and the fairness of synthetic data intervene with each other and identify the areas for future work.…”
Get full text

Multi-key fully homomorphic encryption scheme based on NTRU bootstrapping by ZHENG Junhua, JIANG Hongwei, LIU Rong, LI Yixiu, LI Wen, WENG Jian

“…Finally, the potential application of the scheme in cross-departmental supervision scenarios of multi-industry and multi-source data sales data was explored, which helped the tax department to realize tax verification under the premise of protecting data privacy, and helped promote the digital transformation and healthy development of various industries.…”
Get full text

SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments by Kamran Ahmad Awan, Ikram Ud Din, Ahmad Almogren, Ali Nawaz, Muhammad Yasar Khan, Ayman Altameem

“…The SecEdge framework integrates transformer-based models for efficient handling of long-range dependencies and Graph Neural Networks (GNNs) for modeling relational data, coupled with federated learning to ensure data privacy and reduce latency. The adaptive learning mechanism continuously updates model parameters to counter evolving cyber threats. …”
Get full text

EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks by Sakshi Patni, Joohyung Lee

“…The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. …”
Get full text

Multi-authority attribute hidden for electronic medical record sharing scheme based on blockchain by Lin JIN, Youliang TIAN

“…Currently, there is no data exchanging and sharing between different hospitals, and it is easy to form data islands.At the same time, regional medical data contains a large amount of sensitive information of patients.The public acquisition, sharing and circulation of these data will lead to malicious tampering, theft, abuse and loss of ownership, thereby revealing patient privacy.In addition, the size of medical data is enormous and the data is unstructured, then it is more difficult to prevent and hold accountable some highly targeted malicious attacks, such as malicious attacks on medical data theft, tampering, and extortion.In view of the above problems, a blockchain-based on multi-authority attribute hidden electronic medical record sharing scheme was proposed to achieve fine-grained access to shared electronic medical records while ensuring patient privacy.The Multi-Authorization Attribute Encryption (MA-ABE) algorithm was introduced, which used multi-authority organizations to manage decentralized attributes.It also used hash functions to identify different users, in order to effectively resist collusion attacks between users with different authorizations.Besides, the linear secrets sharing scheme (LSSS) was used to realize partial hiding of attributes, and the attributes were divided into two parts:attribute name and attribute value.In addition, combined with the characteristics of blockchain openness, transparency and tamper-proof, the design of access policy can update the algorithm.Based on the access policy update algorithm, the policy block was added.The new access policy was uploaded to the blockchain to form a policy update traceability chain, which can realize distributed and reliable access control management under the condition of hidden policy.It can also support data privacy protection at the same time, and traceability of user behavior.The theoretical proof and experimental analysis have proved that this scheme protect attribute privacy effectively, while reduces computational overhead.…”
Get full text

A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023 by Shalini Kumari, Chander Prabha, Asif Karim, Md. Mehedi Hassan, Sami Azam

“…FL provides a collaborative way to improve AD using distributed data sources and data privacy.…”
Get full text

Navigating the landscape of remote patient monitoring in Canada: trends, challenges, and future directions by Khayreddine Bouabida, Khayreddine Bouabida, Khayreddine Bouabida, Khayreddine Bouabida, Breitner Gomes Chaves, Enoch Anane, Navaal Jagram

“…We explore the regulatory, technical, and operational challenges that RPM faces, including critical issues around data privacy, security, and interoperability, factors essential for sustainable integration. …”
Get full text

Federated learning and information sharing between competitors with different training effectiveness by Jiajun Meng, Jing Chen, Dongfang Zhao

“…Federated Learning (FL) is an innovative technique that allows multiple firms to collaborate in training machine learning models while preserving data privacy. This is especially important in industries where data is sensitive or subject to regulations like the General Data Protection Regulation (GDPR). …”
Get full text

Object Re-Identification Based on Federated Incremental Subgradient Proximal Optimization by Li Kang, Chuanghong Zhao, Jianjun Huang

“…Federated learning, as a distributed machine learning framework, can utilize dispersed data for model training without sharing raw data, thereby reducing communication costs and ensuring data privacy. However, the real statistical heterogeneity in federated object re-identification leads to domain shift issues, resulting in decreased performance and generalization ability of the ReID model. …”
Get full text