Privacy protection method on time-series data publication by Dong YU, Hai-yan KANG

“…A differential privacy model was proposed based on the sampling filtering and the mechanism of evaluation.Firstly,fixed sampling method was used to sample the original data and the non-sampling data be published directly.Secondly,for the sampling date,utilize the differential privacy mechanism to add the noise.Then,use Kalman to correct the sampling date.Finally,use the mutual information to evaluate data under different sampling intervals.Through the experiment,it is proved that the mechanism can achieve a good balance between the practicality and protective.…”
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Using the Ethereum Blockchain in the Internet of Things Network for IT Diagnostics by U. A. Vishniakou, YiWei Xia, Chuyue Yu

“…The article discusses the use of Ethereum blockchain technology in the Internet of Things (IoT) network for IT diagnostics of patients, which increases data security and user privacy. …”
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Synthetic data for privacy-preserving clinical risk prediction by Zhaozhi Qian, Thomas Callender, Bogdan Cebere, Sam M. Janes, Neal Navani, Mihaela van der Schaar

“…Compared with other privacy-enhancing approaches—such as federated learning—analyses performed on synthetic data can be applied downstream without modification, such that synthetic data can act in place of real data for a wide range of use cases. …”
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Aggregated privacy-preserving auditing for cloud data integrity by Kai HE, Chuan-he HUANG, Xiao-mao WANG, Jing WANG, Jiao-li SHI

“…To solve the problem of data integrity in cloud storage,an aggregated privacy-preserving auditing scheme was proposed.To preserve data privacy against the auditor,data proof and tag proof were encrypted and combined by using the bilinearity property of the bilinear pairing on the cloud server.Furthermore,an efficient index mechanism was designed to support dynamic auditing,which could ensure that data update operations did not lead to high additional computation or communication cost.Meanwhile,an aggregation method for different proofs was designed to handle multiple auditing requests.Thus the proposed scheme could also support batch auditing for multiple owners and multiple clouds and multiple files.The communication cost of batch auditing was independent of the number of auditing requests.The theoretical analysis and experimental results show that the proposed scheme is provably secure.Compared with existing auditing scheme,the efficacy of the proposed individual auditing and batch auditing improves 21.5% and 31.8% respectively.…”
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Differential Privacy and Collective Bargaining over Workplace Data by Sandy J.J. Gould

“…I argue that using differential privacy, a technique for processing data that makes it harder to determine who contributed data to a dataset, would remove an obstacle to employers sharing workplace data with worker representatives.…”
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Research on data integration privacy preservation mechanism for DaaS by Zhi-gang ZHOU, Hong-li ZHANG, Xiang-zhan YU, Pan-pan LI

“…In addition, the corres ding fake data set was used to assure the balanced distribution of data in each part, which realized privacy protection of data integration. …”
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Aggregated privacy-preserving auditing for cloud data integrity by Kai HE, Chuan-he HUANG, Xiao-mao WANG, Jing WANG, Jiao-li SHI

“…To solve the problem of data integrity in cloud storage,an aggregated privacy-preserving auditing scheme was proposed.To preserve data privacy against the auditor,data proof and tag proof were encrypted and combined by using the bilinearity property of the bilinear pairing on the cloud server.Furthermore,an efficient index mechanism was designed to support dynamic auditing,which could ensure that data update operations did not lead to high additional computation or communication cost.Meanwhile,an aggregation method for different proofs was designed to handle multiple auditing requests.Thus the proposed scheme could also support batch auditing for multiple owners and multiple clouds and multiple files.The communication cost of batch auditing was independent of the number of auditing requests.The theoretical analysis and experimental results show that the proposed scheme is provably secure.Compared with existing auditing scheme,the efficacy of the proposed individual auditing and batch auditing improves 21.5% and 31.8% respectively.…”
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Survey of artificial intelligence data security and privacy protection by Kui REN, Quanrun MENG, Shoukun YAN, Zhan QIN

“…Artificial intelligence and deep learning algorithms are developing rapidly.These emerging techniques have been widely used in audio and video recognition, natural language processing and other fields.However, in recent years, researchers have found that there are many security risks in the current mainstream artificial intelligence model, and these problems will limit the development of AI.Therefore, the data security and privacy protection was studied in AI.For data and privacy leakage, the model output based and model update based problem of data leakage were studied.In the model output based problem of data leakage, the principles and research status of model extraction attack, model inversion attack and membership inference attack were discussed.In the model update based problem of data leakage, how attackers steal private data in the process of distributed training was discussed.For data and privacy protection, three kinds of defense methods, namely model structure defense, information confusion defense and query control defense were studied.In summarize, the theoretical foundations, classic algorithms of data inference attack techniques were introduced.A few research efforts on the defense techniques were described in order to provoke further research efforts in this critical area.…”
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A Review of Differential Privacy in Individual Data Release by Jun Wang, Shubo Liu, Yongkai Li

“…The rapid development of mobile technology has improved users' quality of treatment, and tremendous amounts of medical information are readily available and widely used in data analysis and application, which bring on serious threats to users' privacy. …”
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Data driven healthcare insurance system using machine learning and blockchain technologies by Irum Matloob, Shoab Khan, Bushra Bashir, Rukaiya Rukaiya, Javed Ali Khan, Hessa Alfraihi

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Decentralized big data mining: federated learning for clustering youth tobacco use in India by Rahul Haripriya, Nilay Khare, Manish Pandey, Sreemoyee Biswas

“…The analysis employs three clustering algorithms K-Means, DBSCAN, and Hierarchical Clustering within a federated learning framework, which ensures that sensitive public health data remains decentralized and private. Federated learning enables collaborative analysis across different regions by sharing only model parameters rather than raw data, thus enhancing privacy. …”
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Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation by Alper Karamanlıoğlu, Berkan Demirel, Onur Tural, Osman Tufan Doğan, Ferda Nur Alpaslan

“…We evaluate the system through two studies: (1) a benchmark of 750+ USMLE-style questions validating the medical reasoning of fine-tuned LLMs; and (2) a real-world case study (<i>n</i> = 132, 75.8% first-pass agreement) using de-identified MIMIC-III data to assess triage accuracy and responsiveness. …”
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Blockchain Approach for Healthcare Using Fog Topology and Lightweight Consensus by Aya Laouamri, Sarra Cherbal, Yacine Mosbah, Chahrazed Benrebbouh, Kamir Kharoubi

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Privacy Auditing of Lithium-Ion Battery Ageing Model by Recovering Time-Series Data Using Gradient Inversion Attack in Federated Learning by Kaspars Sudars, Ivars Namatevs, Arturs Nikulins, Kaspars Ozols

“…The exchange of gradients is a widely used method in modelling systems for machine learning (e.g., distributed training, federated learning) in privacy-sensitive domains. …”
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Privacy self-management and the issue of privacy externalities: of thwarted expectations, and harmful exploitation by Simeon de Brouwer

“…This term, related to similar concepts from the literature on privacy such as ‘networked privacy’ or ‘data pollution’, is used here to bring to light the incentives and exploitative dynamics behind a phenomenon which, I demonstrate, benefits both the user and the data controller to the detriment of third-party data subjects. …”
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Privacy Centric Offline Chatbot using Large Language Models by K. Anjali, K. Vipunsai, K. Ruchitha, M. Bhavani, Ch. China Subba Reddy

“…They also might collect and store the data leading to privacy breaches. This research paper focuses on these problems. …”
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A Privacy-Preserving RL-Based Secure Charging Coordinator Using Efficient FL for Smart Grid Home Batteries by Amr A. Elshazly, Islam Elgarhy, Mohamed Mahmoud, Mohamed I. Ibrahem, Maazen Alsabaan

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Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD. by Yannick Marcon, Tom Bishop, Demetris Avraam, Xavier Escriba-Montagut, Patricia Ryser-Welch, Stuart Wheater, Paul Burton, Juan R González

“…DataSHIELD uses Opal which is a data integration system used by epidemiological studies and developed by the OBiBa open source project in the domain of bioinformatics. …”
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A Hybrid Brain Tumor Classification Using FL With FedAvg and FedProx for Privacy and Robustness Across Heterogeneous Data Sources by N. Sivakumar, Ahmad Raza Khan, Syed Umar, R. N. Ravikumar, I. Bremnavas, Munindra Lunagaria, Krunal Vaghela, Ghanshyam G. Tejani, Sunil Kumar Sharma

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A Hybrid Framework for Enhancing Privacy in Blockchain-Based Personal Data Sharing using Off-Chain Storage and Zero-Knowledge Proofs by Godwin Mandinyenya, Vusumuzi Malele

“…However, its widespread adoption is constrained by challenges such as limited scalability, privacy concerns, and conflicts with regulatory frameworks like the General Data Protection Regulation (GDPR). …”
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