Federated LeViT-ResUNet for Scalable and Privacy-Preserving Agricultural Monitoring Using Drone and Internet of Things Data by Mohammad Aldossary, Jaber Almutairi, Ibrahim Alzamil

“…New drone photography and IoT sensors offer quick, high-resolution, multimodal agricultural data collecting. Regional diversity, data heterogeneity, and privacy problems make it hard to conclude these data. …”
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Enhancing Data Privacy Protection and Feature Extraction in Secure Computing Using a Hash Tree and Skip Attention Mechanism by Zizhe Zhou, Yaqi Wang, Lin Cong, Yujing Song, Tianyue Li, Meishu Li, Keyi Xu, Chunli Lv

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Unpacking Youth Privacy Management in AI Systems: A Privacy Calculus Model Analysis by Austin Shouli, Ankur Barthwal, Molly Campbell, Ajay Kumar Shrestha

“…The increasing use of Artificial Intelligence (AI) in daily life has introduced substantial issues in protecting user privacy, particularly for young digital citizens. …”
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Using UMAP for Partially Synthetic Healthcare Tabular Data Generation and Validation by Carla Lázaro, Cecilio Angulo

“…The proposed method is validated on prostate and breast cancer data sets, showing its effectiveness in completing and augmenting incomplete data sets using fully available references. …”
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Blockchain-enabled data governance for privacy-preserved sharing of confidential data by Jingchi Zhang, Anwitaman Datta

“…Our novel ABE encryption system supports multi-authority use cases while hiding access policy and ensuring identity privacy, which also protects data sharing against corrupt authorities. …”
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Combining observational and experimental data for causal inference considering data privacy by Mann Charlotte Z., Sales Adam C., Gagnon-Bartsch Johann A.

“…We find that leveraging transformed observational data in treatment effect estimation can still improve estimation over only using data from an RCT.…”
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Dynamic Cohort Formation with Hierarchical Blockchain Using GDP for Enhanced FL by Sunila Fatima Ahmad, Zahra Abbas, Madiha Haider Syed, Adeel Anjum, Semeen Rehman

“…In addition, Gaussian Differential Privacy (GDP) is used as a privacy-preserving mechanism that adds controlled noise to the data or model updates to protect individual data points from being inferred by adversaries. …”
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Privacy in the Internet: Myth or reality by Mikarić Bratislav, Marković Marija, Trajković Dušan

“…The present time, unthinkable without using Internet - from e-mail, through social networks, cloud services, GPS, to YouTube and mobile computing in business, as well as on a private level, poses a question: Is there a way to protect data and their privacy on the Internet? …”
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Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU by Pascal Riedel, Kaouther Belkilani, Manfred Reichert, Gerd Heilscher, Reinhold von Schwerin

“…We propose a bottom-up, privacy-preserving prediction method using differential privacy (DP) to enhance data privacy for energy analytics on the customer side. …”
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Evaluating the Impact of Artificial Intelligence Tools on Enhancing Student Academic Performance: Efficacy Amidst Security and Privacy Concerns by Jwern Tick Kiet Phua, Han-Foon Neo, Chuan-Chin Teo

“…This research investigates the perceptions and attitudes of students towards the use of AI tools in their academic activities, focusing on constructs such as perceived usefulness, the perceived ease of use, security and privacy concerns, and both positive and negative attitudes towards AI. …”
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Private detection of relatives in forensic genomics using homomorphic encryption by Fillipe D. M. de Souza, Hubert de Lassus, Ro Cammarota

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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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PPFL-DCS: Privacy-Preserving Federated Learning Using Neural Transformer and Leveraging Dynamic Client Selection to Accommodate Data Diversity by Nakul Mehta, Nitesh Bharot, John G. Breslin, Priyanka Verma

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A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural Network by Jia Jia, Sathiya Sekar Kumarasamy, Kiran Sree Pokkuluri, K. Suresh Kumar, Thella Preethi Priyanka, Feng Wang

“…So, in our model after completing the node authentication and trust detection, privacy preservation of data is performed using Optimal Key-aided Data Sanitization (OPDS). …”
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A survey of individuals’ willingness to share real-world data postmortem with researchers by Rachele M. Hendricks-Sturrup, Christine Y. Lu

Subjects: “…Data use…”
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Proactive Data Categorization for Privacy in DevPrivOps by Catarina Silva, João P. Barraca, Paulo Salvador

“…PsDC is a data-categorization model designed for integration with the DevPrivOps methodology and for use in privacy-quantification models. …”
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Enhancing healthcare data privacy and interoperability with federated learning by Adil Akhmetov, Zohaib Latif, Benjamin Tyler, Adnan Yazici

“…Unlike traditional centralized learning (CL) solutions that require data centralization, our platform uses local model learning, which naturally improves data privacy. …”
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Secure data sharing technology of medical privacy data in the Web 3.0 by Shusheng Guo, Cheng Chen, Qing Tong

“…Finally, data users apply to multiple parties for joint secure computing to obtain the use of private data. …”
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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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Privacy guarantees for personal mobility data in humanitarian response by Nitin Kohli, Emily Aiken, Joshua E. Blumenstock

“…Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. …”
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