Ethereum Blockchain Interconnectivity for Dynamic and Privacy-Preserving Access Control by Christian Delgado-von-Eitzen, Manuel Jose Fernandez-Iglesias, Luis Anido-Rifon, Martin Llamas-Nistal

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What we do with data: a performative critique of data 'collection' by Garfield Benjamin

“…How do terms and practices relate in defining the norms of data in society? This article undertakes a critique of data collection using data feminism and a performative theory of privacy: as a resource, an objective discovery and an assumption. …”
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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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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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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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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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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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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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A hybrid encryption algorithm based approach for secure privacy protection of big data in hospitals by Wei Li, Qian Huang

“…First, collect hospital big data including hospital medical business system, mobile wearable devices and big health data; Secondly, use byte changes to compress hospital big data to achieve safe transmission of hospital big data; Then, the hospital sender uses the AES session key to encrypt the hospital big data and the ECC public key to encrypt the AES session key, uses SHA-1 to calculate the hash value of the medical big data, and uses the ECC public key to sign the hash value; The hospital receiver uses the ECC private key to verify the signature, and decrypts the AES session key using the ECC private key. …”
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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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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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Sharing extended summary data from contemporary genetics studies is unlikely to threaten subject privacy. by Silviu-Alin Bacanu

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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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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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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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A Combined Approach of Heat Map Confusion and Local Differential Privacy for the Anonymization of Mobility Data by Christian Dürr, Gabriele S. Gühring

“…Mobility data plays a crucial role in modern location-based services (LBSs), yet it poses significant privacy risks, as it can reveal highly sensitive information such as home locations and behavioral patterns. …”
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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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