Privacy-Preserving Data Sharing in Telehealth Services by Ammar Odeh, Eman Abdelfattah, Walid Salameh

“…Telehealth services, while providing immense benefits in terms of accessibility and efficiency, introduce complex challenges in maintaining data privacy and security. This paper proposes a privacy-preserving framework for secure data sharing within telehealth services, employing blockchain technology and advanced cryptographic techniques. …”
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Privacy-Preserving Synthetic Data Generation Method for IoT-Sensor Network IDS Using CTGAN by Saleh Alabdulwahab, Young-Tak Kim, Yunsik Son

“…This study proposes a privacy-preserving synthetic data generation method using a conditional tabular generative adversarial network (CTGAN) aimed at maintaining the utility of IoT sensor network data for IDS while safeguarding privacy. …”
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Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models. by Michael J Tildesley, Sadie J Ryan

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Improving Data Privacy in the Internet of Things by Using Artificial Immune System, in Regard with the Internet Limitations by Marzieh Faridi Masouleh, Ali Harounabadi, Asal Sayyad

“…Studies show that the lack of a complete, efficient and standard model has caused many security flaws in these networks. By using the Internet limitations, this study discusses how to create a target function to protect data privacy in the Internet of Things. …”
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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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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.

“…However, many observational datasets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. …”
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Exploring the association between cultural values and the intention to use digital data wallets by Varvara Keba, Tiago Oliveira, Melanie Trabandt

“…We developed a model to examine the intention to use digital data wallets – new privacy-enhancing tools based on the privacy calculus theory and Hofstede's cultural values. …”
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Enhancing privacy in IoT-based healthcare using provable partitioned secure blockchain principle and encryption by C. S. Madhumathi, K. Vishnu Kumar

“…Data sharing is a significant concern in healthcare because of privacy leakage and security issues. …”
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Perturbation based privacy preservation and classification using Jaya Algorithm and Dragonfly Inspired Algorithm by Dipanwita Sen, Bhupati Bhusan Mishra, Prasant Kumar Pattnaik

“…That is why, privacy preservation of data for healthcare dataset is a significant consideration. …”
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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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Doubly Structured Data Synthesis for Time-Series Energy-Use Data by Jiwoo Kim, Changhoon Lee, Jehoon Jeon, Jungwoong Choi, Joseph H. T. Kim

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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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DPLQ: Location‐based service privacy protection scheme based on differential privacy by Qingyun Zhang, Xing Zhang, Mingyue Wang, Xiaohui Li

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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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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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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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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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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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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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