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

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A robust and personalized privacy-preserving approach for adaptive clustered federated distillation by Mai Shawkat, Zainab H. Ali, Mofreh Salem, Ali El-desoky

“…Meta-learning is used in each cluster to enhance the personalization of the local models and the classification accuracy of the non-independent and Identically distributed (non-IID) data distributions. …”
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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. …”
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Privacy Issues, Attacks, Countermeasures and Open Problems in Federated Learning: A Survey by Blessing Guembe, Sanjay Misra, Ambrose Azeta

“…Aim This study presents a cutting-edge survey on privacy issues, security attacks, countermeasures and open problems in FL.Methodology The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was used to determine the research domain, establish a search query, and analyze all retrieved articles from the selected scientific databases (i.e. …”
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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.…”
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Light weighted privacy protection ViT inference framework based on secret sharing by MA Min, FU Yu, HUANG Kai, JIA Xiaofeng

“…The ViT (vision transformer) inference framework, which was widely used in image processing, was found to have a risk of leaking user privacy data. …”
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Eisdspa: An Efficient and Secure Blockchain-Based Donation Scheme With Privacy Protection and Auditability by Yong Zhou, Hong Lei, Zijian Bao

“…However, traditional donation systems, typically centralized, are prone to issues such as data redundancy, vulnerability to single-point failures, and a deficiency in transparency and traceability. …”
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Sharing images of children on social media: British motherhood influencers and the privacy paradox. by Katherine Baxter, Barbara Czarnecka

“…This study examines the extent to which popular British motherhood influencers infringe on their children's privacy by posting images of them online. We conducted a content analysis of 5,253 Instagram posts from ten UK-based influencers, supplemented by self-reported data from these influencers. …”
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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. …”
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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. …”
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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.…”
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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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Synergistic Disruption: Harnessing AI and Blockchain for Enhanced Privacy and Security in Federated Learning by Sandi Rahmadika, Winda Agustiarmi, Ryan Fikri, Bruno Joachim Kweka

“…Blockchain technology makes self-executing agreements possible by enabling smart contracts, which reduce the need for middlemen and increase efficiency by precisely encoding contractual terms in code. By using AI oracles, these contracts can communicate with outside data sources and make well-informed decisions based on actual occurrences. …”
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A lightweight and secure authentication and privacy protection scheme for internet of medical things by Sanjay Kumar, Kumar Abhishek, Shitharth Selvarajan

“…However, transmitting sensitive patient data through IoMT devices raises significant security and privacy concerns. …”
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The Looming Privacy Challenges Posed by Commercial Satellite Imaging: Remedies and Research Directions by Nathanael Denis, Roberto Di Pietro

“…While these technologies benefit applications such as natural disaster monitoring and precision agriculture, they also introduce novel privacy risks. This perspective paper provides several contributions: we start by analyzing privacy risks using the LINDDUN privacy framework, categorizing threats like linking, identification, and data disclosure. …”
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Blockchain-Enabled Zero Trust Architecture for Privacy-Preserving Cybersecurity in IoT Environments by Mohammed A. Aleisa

“…It then uses blockchain technology for recording unalterable data of identity and access management while Zero-Knowledge Proofs (ZKP) ensures authentication and verification without revealing sensitive information. …”
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Self-Disclosure on Professional Social Networking Sites: A Privacy Calculus Perspective by Imairi Eitiveni, Achmad Nizar Hidayanto, Yuvitri Annisa Dwityafani, Larastri Kumaralalita

“…A model contextualizing privacy calculus theory combined with the trust factor was developed and evaluated using 661 quantitative data collected through a questionnaire. …”
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Privacy preserving strategies for electronic health records in the era of large language models by Jitendra Jonnagaddala, Zoie Shui-Yee Wong

“…Risks can be reduced by using strategies like privacy-preserving locally deployed LLMs, synthetic data generation, differential privacy, and deidentification. …”
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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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