The Law Reform Regarding the Regulation of Medical Use of Artificial Intelligence and the Protection of Patient Privacy in the Utilization of Artificial Intelligence in Health Care by Meidiawaty Fusia

“…This reform should include restrictions on the use of artificial intelligence in the health sector, as well as guarantees of confidentiality of patient data.…”
Get full text

Mobile Phone Network Data in the COVID-19 era: A systematic review of applications, socioeconomic factors affecting compliance to non-pharmaceutical interventions, privacy implicat... by Mohammed Okmi, Tan Fong Ang, Muhammad Faiz Mohd Zaki, Chin Soon Ku, Koo Yuen Phan, Irfan Wahyudi, Lip Yee Por

“…<h4>Background</h4>The use of traditional mobility datasets, such as travel surveys and census data, has significantly impacted various disciplines, including transportation, urban sensing, criminology, and healthcare. …”
Get full text

A Proposed Vision for Using Artificial Intelligence in Enhancing Strategic Value of Human Resources by Nadera Hourani

“…Yet, there are significant challenges in the form of algorithmic bias, data privacy concerns, and organizational readiness. …”
Get full text

Federated learning in food research by Zuzanna Fendor, Bas H.M. van der Velden, Xinxin Wang, Andrea Jr. Carnoli, Osman Mutlu, Ali Hürriyetoğlu

“…The use of machine learning in food research is sometimes limited due to data sharing obstacles such as data ownership and privacy requirements. …”
Get full text

Federated meta learning: a review by Chuanyao ZHANG, Shijing SI, Jianzong WANG, Jing XIAO

“…With the popularity of mobile devices, massive amounts of data are constantly produced.The data privacy policies are becoming more and more specified, the flow and use of data are strictly regulated.Federated learning can break data barriers and use client data for modeling.Because users have different habits, there are significant differences between different client data.How to solve the statistical challenge caused by the data imbalance becomes an important topic in federated learning research.Using the fast learning ability of meta learning, it becomes an important way to train different personalized models for different clients to solve the problem of data imbalance in federated learning.The definition and classification of federated learning, as well as the main problems of federated learning were introduced systematically based on the background of federated learning.The main problems included privacy protection, data heterogeneity and limited communication.The research work of federated metalearning in solving the heterogeneous data, the limited communication environment, and improving the robustness against malicious attacks were introduced systematically starting from the background of federated meta learning.Finally, the summary and prospect of federated meta learning were proposed.…”
Get full text

ETHICAL DIMENSIONS OF HUMAN RESOURCES AUDITING IN THE DIGITAL ERA by Alice STEFANESCU, Daria MARIN

Get full text

Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis by Raja Waseem Anwar, Mohammad Abrar, Abdu Salam, Faizan Ullah

“…Using an FL approach, multiple IoT nodes collaboratively train a global LSTM model without exchanging raw data, thereby addressing privacy concerns and improving detection capabilities. …”
Get full text

Dynamic game and reliable recommendation based transferring reputation mechanism for mobile cloud computing by Hui LIN, Mengyang YU, Youliang TIAN, Yijie HUANG

“…The booming development of the mobile internet and cloud computing leads to the emerging of many mobile cloud platforms based services.However,since mobile users store lots of data and privacy information in the cloud when they are using the mobile cloud services,they are facing multiple increasingly serious security threats such as data leaks and privacy exposures.The data security and privacy protection was investigated in mobile cloud computing,aiming at the internal bad mouthing attacks and mobile attacks.A dynamic game and reliable recommendation based transferring reputation mechanism was proposed.First,a dynamic game based recommendation incentive mechanism was proposed.Secondly,a reliable recommendation reputation evaluation model was established based on the incentive mechanism.Last,a novel transferring reputation mechanism was proposed that combined the above mentioned incentive mechanism and reputation evaluation model.Simulation results demonstrate the proposed transferring reputation mechanism can defend against the internal bad mouthing attacks and mobile attacks effectively,enhance the credibility of mobile terminals and improve the data security and privacy protection of mobile cloud services.…”
Get full text

Artificial intelligence in neuroimaging: Opportunities and ethical challenges by Neha Brahma, S. Vimal

“…Issues such as algorithmic bias, data privacy, and the interpretability of AI-driven insights must be addressed to ensure that these technologies are used responsibly and equitably. …”
Get full text

Learning-augmented sketching offers improved performance for privacy preserving and secure GWAS by Junyan Xu, Kaiyuan Zhu, Jieling Cai, Can Kockan, Natnatee Dokmai, Hyunghoon Cho, David P. Woodruff, S. Cenk Sahinalp

“…To address this, methods like SkSES use sketching for genome-wide association studies (GWAS) across distributed datasets while maintaining privacy. …”
Get full text

Online medical privacy protection strategy under information value-added mechanism by Shengzhi MING, Jianming ZHU, Zhiyuan SUI, Xian ZHANG

“…China’s economic level and people’s living standards have developed rapidly in recent years, and the medical level and medical technology have made breakthroughs continuously.With the promotion and deepening of“Internet Plus” to business model innovation in various fields, the development of “Internet Plus” medical has been rapidly developed.Due to the continuous development of data processing technologies such as machine learning and data mining, the risk of users’ personal medical data disclosure in the process of online medical treatment has also attracted the attention of researchers.Considering the deductibility of information, the discount mechanism was adopted to describe the change of user’s private information value in different stages of the game.Combined with the current research status in the field of online medical privacy protection motivation, how to mobilize the enthusiasm of both players from the level of privacy protection motivation was explored with game analysis.In view of the game characteristics of users’ strong willingness to continually use the online medical platform and intermittently provide privacy, the repeated game method was adopted to better describe the game process between users and the online medical platform.The tendency change law of the players on both sides of the game was obtained.Moreover, the Nash equilibrium of the game model was analyzed under different model parameters and the change trend of the game strategy of both sides with the progress of the game stage.When the parameters were met 2(c_p-c_n)≥l_p(p_n-p_p), the user started to choose from “agree to share private data” to “refuse to share private data”.The above conclusion was verified by simulation experiments.Based on the above conclusions, from the perspective of online medical platform and users, policy suggestions on how to realize privacy protection from the level of privacy protection motivation in the process of online medical treatment were given.…”
Get full text

Improved SpaceTwist privacy protection method based on anchor optimization algorithm by Zhen-peng LIU, Xuan ZHAO, Ya-wei DONG, Bin ZHANG

“…With location-based services worldwide used,private location data appealed easily in query process which caused serious security problems.So the introduction of SpaceTwist incremental nearest neighbor query algorithm,proposes protection of privacy method combined with improved SpaceTwist location optimization algorithm.The anchor point authentication server added to distributed system structure,user generate a k anonymous area according to their privacy preference and actual environment,using optimization algorithm to generate the anchor point.Forwarding users use the incremental nearest neighbor query throught the anchor point and accurate.Experiments in road network environment with different data sets show that the privacy protection works well in the algorithm,and own high work efficiency.…”
Get full text

Prioritizing privacy and presentation of supportable hypothesis testing in forensic genetic genealogy investigations by Bruce Budowle, Lee Baker, Antti Sajantila, Kristen Mittelman, David Mittelman

“…However, FGG generated genetic data contain private and sensitive information. Therefore, it is essential to deploy approaches that minimize unnecessary disclosure of these data to mitigate potential risks to individual privacy. …”
Get full text

Verticox+: vertically distributed Cox proportional hazards model with improved privacy guarantees by Florian van Daalen, Djura Smits, Lianne Ippel, Andre Dekker, Inigo Bermejo

“…Abstract Federated learning allows us to run machine learning algorithms on decentralized data when data sharing is not permitted due to privacy concerns. …”
Get full text

Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism by Yikai Huang, Jinchuan Tang, Shuping Dang

Get full text

Privacy challenges of automated vehicles: Merging contextual integrity and responsible innovation frameworks by Dasom Lee, Le Anh Nguyen Long

“…MCI considers contextual integrity (CI) in tandem with societal preferences and individual level preferences, which is captured using demographic data. Therefore, it captures the individual-level, group-level, and societal-level factors that drive peoples’ preferences regarding AV privacy. …”
Get full text

Balancing Privacy and Utility in Split Learning: An Adversarial Channel Pruning-Based Approach by Afnan Alhindi, Saad Al-Ahmadi, Mohamed Maher Ben Ismail

“…Moreover, training such models using private data is prone to serious privacy risks resulting from inadvertent disclosure of sensitive information. …”
Get full text

A Communication-Efficient Distributed Matrix Multiplication Scheme with Privacy, Security, and Resiliency by Tao Wang, Zhiping Shi, Juan Yang, Sha Liu

“…Inspired by the application of repairing Reed–Solomon (RS) codes in distributed storage and secret sharing, we propose SDMM schemes with reduced communication overhead through the use of trace polynomials. Specifically, these schemes are designed to address three critical concerns: (i) ensuring information-theoretic privacy against collusion among servers; (ii) providing security against Byzantine servers; and (iii) offering resiliency against stragglers to mitigate computing delays. …”
Get full text

FairRAG: A Privacy-Preserving Framework for Fair Financial Decision-Making by Rashmi Nagpal, Unyimeabasi Usua, Rafael Palacios, Amar Gupta

“…These improvements were maintained when using differentially private synthetic data, thus indicating robust privacy and accuracy trade-offs.…”
Get full text

Synergistic Disruption: Harnessing AI and Blockchain for Enhanced Privacy and Security in Federated Learning by Sandi Rahmadika, Winda Agustiarmi, Ryan Fikri, Bruno Joachim Kweka

“…Additionally, there is a lot of potential for improving machine learning and data interchange in terms of privacy, security, and transparency through the integration of blockchain with federated learning. …”
Get full text