An explainable federated blockchain framework with privacy-preserving AI optimization for securing healthcare data by Tanisha Bhardwaj, K. Sumangali

“…Abstract With the rapid growth of healthcare data and the need for secure, interpretable, and decentralized machine learning systems, Federated Learning (FL) has emerged as a promising solution. …”
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DP-FedCMRS: Privacy-Preserving Federated Learning Algorithm to Solve Heterogeneous Data by Yang Zhang, Shigong Long, Guangyuan Liu, Junming Zhang

“…In federated learning, non-independently and non-identically distributed heterogeneous data on the clients can limit both the convergence speed and model utility of federated learning, and gradients can be used to infer original data, posing a threat to user privacy. …”
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Decoding privacy concerns: the role of perceived risk and benefits in personal health data disclosure by Havva Nur Atalay, Şebnem Yücel

“…Results The analysis revealed a significant negative relationship between individuals’ personal health data disclosure behaviour and their privacy concerns. …”
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A synchronous compression and encryption method for massive electricity consumption data privacy preserving by Ruifeng Zhao, Jiangang Lu, Zhiwen Yu, Kaiwen Zeng

“…Our proposed algorithm uses a ternary Logistic-Tent chaotic system for generating a chaotic measurement matrix, allowing simultaneous data compression and encryption of user-side voltage and current data. …”
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Privacy-Preserving Keyword Search With Access Control for Secret Sharing-Based Data Outsourcing by Ahmad Akmal Aminuddin Mohd Kamal, Masamune Okada, Masaya Fujisawa

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Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings by Rahul Haripriya, Nilay Khare, Manish Pandey

“…Abstract Ensuring data privacy in medical image classification is a critical challenge in healthcare, especially with the increasing reliance on AI-driven diagnostics. …”
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Precision-Enhanced and Encryption-Mixed Privacy-Preserving Data Aggregation in Wireless Sensor Networks by Geng Yang, Sen Li, Xiaolong Xu, Hua Dai, Zhen Yang

“…Security is always a hot topic in wireless sensor networks (WSNs). Privacy-preserving data aggregation has emerged as an important concern in designing data aggregation algorithm. …”
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Time-based and privacy protection revocable and traceable data sharing scheme in cloud computing by Jiawei ZHANG, Jianfeng MA, Zhuo MA, Teng LI

“…General ciphertext-policy attribute-based encryption (CP-ABE) provides fine-grained access control for data sharing in cloud computing, but its plaintext formed access policy may cause leakage of private and sensitive data.And revoking a malicious user by accurately tracing the identity according to a leaked decryption key is a huge challenge.Moreover, most of existing revocable schemes incur long user revocation list and low efficiency.To solve these problems, a time-based and privacy preserving revocable and traceable data sharing scheme was proposed based on CP-ABE to support expressive monotonic and partial hidden access policy, large attribute universe by conceal the attribute values in access policy.Time-limited data access control using hierarchical identity-based encryption was achieved to set key valid period for users.Moreover, with the approaches of white-box tracing and binary tree, efficient user tracing and direct revocation with shorter revocation list was realized together with high efficiency via online/offline and verifiable outsourced decryption techniques.Furthermore, the scheme was secure under decisional q-BDHE assumption.Theoretical analysis and extensive experiments demonstrate its advantageous performance in computational and storage cost.…”
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A blockchain-based verifiable CP-ABE scheme for medical data privacy protection by Leilei Zhao, Guofang Dong, Hao Yuan

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Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework by Kamta Nath Mishra, Rajesh Kumar Lal, Paras Nath Barwal, Alok Mishra

“…Data privacy is a crucial concern for individuals using cloud storage services, and cloud service providers are increasingly focused on meeting this demand. …”
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Privacy Risk Assessment of Medical Big Data Based on Information Entropy and FCM Algorithm by Xiaoliang Zhang, Tianwei Guo

“…However, the high sensitivity and privacy of medical data also bring serious security challenges. …”
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A Blockchain-Based Secure Data Transaction and Privacy Preservation Scheme in IoT System by Jing Wu, Zeteng Bian, Hongmin Gao, Yuzhe Wang

“…With the explosive growth of Internet of Things (IoT) devices, massive amounts of heterogeneous data are continuously generated. However, IoT data transactions and sharing face multiple challenges such as limited device resources, untrustworthy network environment, highly sensitive user privacy, and serious data silos. …”
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A Qualitative Study of Researchers Perspective on the Use and Risks of Open Government Data by Emigawaty Emigawaty, Dinda Sukmaningrum, Wiji Nurastuti

“…This study aims to review the potential risks of data openness on government data portals from the perspective of researchers as one of the important actors who use data. …”
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Multi-party summation query method based on differential privacy by Xianmang HE

“…Differential privacy is considered to be a very reliable protection mechanism because it does not require the a prior knowledge for the attacker.However,differential privacy is rarely used in a multi-party environment.In view of this,the differential privacy is applied to the data summation query in multi-party environment.This method was described in detail and proved the security of the method.…”
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EXPLORING USER PERSPECTIVES ON THE APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN FINANCIAL TECHNOLOGY by Sadhana Tiwari, Mohammad Asif, Amar Johri, Mohammad Wasiq, Mohd Imran

“…Structural equation modeling was employed to study the relationships between the adoption of fintech AI and its predictors, namely Perceived Usefulness, Perceived Privacy and Security, Perceived Ease of Use, and Satisfaction of Fintech AI. …”
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Federated Mental Wellbeing Assessment Using Smartphone Sensors Under Unreliable Participation by Gavryel Martis, Ryan McConville

“…The findings suggested that given the widespread use of such devices, FL holds great potential in mood and depression detection while protecting data privacy. …”
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Human Behavior Analysis Using Radar Data: A Survey by Patrycja Miazek, Alicja Zmudzinska, Pawel karczmarek, Adam Kiersztyn

“…In this study, we focus not only on the analysis of human behavior using radar but also on video observations. We review methods of human activity recognition using LiDAR. …”
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Efficient and Privacy-Preserving Decision Tree Inference via Homomorphic Matrix Multiplication and Leaf Node Pruning by Satoshi Fukui, Lihua Wang, Seiichi Ozawa

“…Cloud computing is widely used by organizations and individuals to outsource computation and data storage. …”
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Cultural Differences in the Use of Augmented Reality Smart Glasses (ARSGs) Between the U.S. and South Korea: Privacy Concerns and the Technology Acceptance Model by Se Jung Kim, Yoon Esther Lee, T. Makana Chock

“…Korea = 898) perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention to use ARSGs were impacted by privacy concerns. …”
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A differentially-private mechanism for multi-level data publishing by Wen-jing ZHANG, Hui LI

“…Privacy preserving technology had addressed the problem of privacy leakage during data publishing proc-ess,however,current data publishing technologies mostly focused on publishing privacy preserving data with single level,without considering some scenarios of multi-level users.Therefore,a differentially-private mechanism for multi-level data publishing was proposed.The proposed mechanism employed the Laplace mechanism with different privacy budgets to output results with different privacy protection levels.After the user’s level was determined ac-cording to the charge or privilege of that specific user,the goal that a user with high(low) level can only use the out-put result with low(high) privacy protection level which had low(high) error rate could be achieved.Finally,the evaluation results and security analysis show that our proposed framework can not only prevent from background knowledge attack,but also achieve multi-level data publishing with different error rates effectively .…”
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