Overcoming diagnostic and data privacy challenges in viral disease detection: an integrated approach using generative AI, vision transformers, explainable AI, and federated learning by Asadi Srinivasulu, Anupam Agrawal, Ramchand Vedaiyan

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A comprehensive survey on big data privacy and Hadoop security: Insights into encryption mechanisms and emerging trends by Youness Filaly, Nisrine Berros, Fatna El mendili, Younes El Bouzekri EL idrissi

“…We find weaknesses in existing techniques via a comparative study and provide a hybrid encryption system aimed at secure and efficient data processing in Hadoop settings. Researchers and practitioners searching for scalable, privacy-preserving big data platform solutions should use this paper as a reference.…”
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Developing a Model for Protecting the Privacy of Internet Customers in the Field of Health by Zahra Sharifi, Mohammad Ali Keramati, Mehrzad Minooei

“…In this area, there is sensitive and personal information, and privacy can increase customers’ trust in companies and create a stronger relationship between them.Methods: The target sample was chosen using a criterion-oriented purposeful sampling method. …”
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A hybrid encryption algorithm based approach for secure privacy protection of big data in hospitals by Wei Li, Qian Huang

“…Aiming at the hidden danger of information security caused by the lack of medical big data information security firewall, this paper proposes a security privacy protection method for hospital big data based on hybrid encryption algorithm. …”
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Secondary Use of Electronic Health Record: Opportunities and Challenges by Shahid Munir Shah, Rizwan Ahmed Khan

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When Poor-Quality Data Meet Anonymization Models: Threats and Countermeasures by Abdul Majeed, Seong Oun Hwang

“…For example, skewed data may lead to imbalanced learning when used in machine learning (ML) classifiers. …”
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Navigating the privacy paradox in a digital age: balancing innovation, data collection and ethical responsibility by Martin Jones

“…Purpose – The purpose of this paper is to explore the intersection of generative artificial intelligence (AI), data collection and consumer privacy, highlighting ethical tensions in AI-driven advertising. …”
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Mamba-fusion for privacy-preserving disease prediction by Muhammad Kashif Jabbar, Huang Jianjun, Ayesha Jabbar, Anas Bilal

“…Experimental results on multi-modal clinical measurements, ECG, EEG, clinical notes, and demographic data support the applied framework. We have then used Mamba-Fusion to achieve 92:4% accuracy, 0:91 F-Score, and 0:96 AUC-ROC by keeping the privacy leakage at 0:02 and communication costs to 12:5 MB, which make it superior to conventional FL techniques. …”
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CRYPTOGRAPHIC KEY IMPROVED PRIVACY UNDER THE CONDITIONS OF SOME OF CRYPTOGRAPHIC KEY VALUE DATA LEAK by U. F. Holikau, V. L. Pivovarov

“…The article outlines the possibility of increasing the privacy of cryptographic key generated in the conditions of data leakage of some of its values. …”
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Going global: Comparing Chinese mobile applications’ data and user privacy governance at home and abroad by Lianrui Jia, Lotus Ruan

“…Lastly, we conducted content analysis of the terms of service and privacy policies to establish the app’s data collection, storage, transfer, use, and disclosure measures. …”
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Privacy Protection Based Secure Data Transaction Protocol for Smart Sensor Meter in Smart Grid by Woong Go, SeulKi Choi, Jin Kwak

“…They could then burgle the house. We propose a privacy-enhanced secure data transaction protocol that can protect private data by encrypting them. …”
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IoT medical device risks: Data security, privacy, confidentiality and compliance with HIPAA and COBIT 2019 by Na-ella Khan, Riaan J. Rudman

“…Purpose: This study aimed to develop a comprehensive framework to enable the identification of risks pertaining to data security, privacy and confidentiality when using medical Internet of Things (IoT) devices. …”
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Sharing extended summary data from contemporary genetics studies is unlikely to threaten subject privacy. by Silviu-Alin Bacanu

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ChatAnalysis revisited: can ChatGPT undermine privacy in smart homes with data analysis? by Jüttner Victor, Fleig Arthur, Buchmann Erik

“…While empowering users, this raises critical privacy concerns when used to analyze data from personal spaces, such as smart-home environments. …”
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Metric and classification model for privacy data based on Shannon information entropy and BP neural network by Yihan YU, Yu FU, Xiaoping WU

“…The trained BP neural network was used to output the classification result of privacy data without pre-determining the metric weight. …”
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Metric and classification model for privacy data based on Shannon information entropy and BP neural network by Yihan YU, Yu FU, Xiaoping WU

“…The trained BP neural network was used to output the classification result of privacy data without pre-determining the metric weight. …”
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Airbnb in New York City: whose privacy rights are threatened by a Government Data grab? by T. Hofmann

“…Using Local Law 146 as a lens, this Note examines the privacy issues implicated by data- collection laws and discusses which parties can assert these privacy rights, particularly given recent changes in third-party doctrine jurisprudence. …”
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Medical data privacy protection based on blockchain asymmetric encryption algorithm and generative adversarial network by Yuanyuan Gao, Jin-whan Kim

“…In the blockchain environment, asymmetric encryption algorithms are used to generate private keys and public keys to encrypt user privacy data. …”
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Multi-function supported privacy protection data aggregation scheme for V2G network by Baiji HU, Xiaojuan ZHANG, Yuancheng LI, Rongxin LAI

“…In view of the problem that the functions of the current privacy protection data aggregation scheme were insufficient to meet the increasingly rich application requirements, a multi-function supported privacy protection data aggregation (MFPDA) scheme for V2G network was proposed.By using cryptographic algorithms such as BGN, BLS, and Shamir’s secret sharing, as well as fog computing and consortium blockchain technology, multiple security functions like fault tolerance, resistance to internal attacks, batch signature verification, no need for trusted third parties, and multiple aggregation functions were integrated into one privacy protection data aggregation scheme.Security analysis shows that the proposed scheme can protect data aggregation’s security, privacy and reliability.The performance evaluation shows that the introduction of fog computing can significantly reduce the computing overhead of the control center, and the reduction rate can be as high as 66.6%; the improvement of the consortium blockchain can effectively reduce the communication and storage overhead of the system, and the reduction rate can reach 16.7% and 24.9% respectively.…”
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Multi-function supported privacy protection data aggregation scheme for V2G network by Baiji HU, Xiaojuan ZHANG, Yuancheng LI, Rongxin LAI

“…In view of the problem that the functions of the current privacy protection data aggregation scheme were insufficient to meet the increasingly rich application requirements, a multi-function supported privacy protection data aggregation (MFPDA) scheme for V2G network was proposed.By using cryptographic algorithms such as BGN, BLS, and Shamir’s secret sharing, as well as fog computing and consortium blockchain technology, multiple security functions like fault tolerance, resistance to internal attacks, batch signature verification, no need for trusted third parties, and multiple aggregation functions were integrated into one privacy protection data aggregation scheme.Security analysis shows that the proposed scheme can protect data aggregation’s security, privacy and reliability.The performance evaluation shows that the introduction of fog computing can significantly reduce the computing overhead of the control center, and the reduction rate can be as high as 66.6%; the improvement of the consortium blockchain can effectively reduce the communication and storage overhead of the system, and the reduction rate can reach 16.7% and 24.9% respectively.…”
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