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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Sharing extended summary data from contemporary genetics studies is unlikely to threaten subject privacy. by Silviu-Alin Bacanu

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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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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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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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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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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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Practical and privacy-preserving geo-social-based POI recommendation by Qi Xu, Hui Zhu, Yandong Zheng, Fengwei Wang, Le Gao

“…Specifically, we first utilize the quad tree to organize geographic data and the MinHash method to index social data. …”
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Privacy-preserving federated learning framework with dynamic weight aggregation by Zuobin YING, Yichen FANG, Yiwen ZHANG

“…There are two problems with the privacy-preserving federal learning framework under an unreliable central server.① A fixed weight, typically the size of each participant’s dataset, is used when aggregating distributed learning models on the central server.However, different participants have non-independent and homogeneously distributed data, then setting fixed aggregation weights would prevent the global model from achieving optimal utility.② Existing frameworks are built on the assumption that the central server is honest, and do not consider the problem of data privacy leakage of participants due to the untrustworthiness of the central server.To address the above issues, based on the popular DP-FedAvg algorithm, a privacy-preserving federated learning DP-DFL algorithm for dynamic weight aggregation under a non-trusted central server was proposed which set a dynamic model aggregation weight.The proposed algorithm learned the model aggregation weight in federated learning directly from the data of different participants, and thus it is applicable to non-independent homogeneously distributed data environment.In addition, the privacy of model parameters was protected using noise in the local model privacy protection phase, which satisfied the untrustworthy central server setting and thus reduced the risk of privacy leakage in the upload of model parameters from local participants.Experiments on dataset CIFAR-10 demonstrate that the DP-DFL algorithm not only provides local privacy guarantees, but also achieves higher accuracy rates with an average accuracy improvement of 2.09% compared to the DP-FedAvg algorithm models.…”
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Sinergi Pemanfaatan Big Data dengan Sharing Data Privacy Pengguna: Efek Menawarkan Kompensasi dalam Meyakinkan Pengguna E-Wallet by Christina Sudyasjayanti, Laili Rahmania

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Solving the Privacy-Equity Trade-off in Data Sharing By Using Homophily, Diversity, and <italic>t</italic>-Closeness Based Anonymity Algorithm by Abdul Majeed, Seong Oun Hwang

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