Genomics data sharing

Kumuthini, Judit; Zass, Lyndon; Chaouch, Melek; Fadlelmola, Faisal M.; Mulder, Nicola; Radouani, Fouzia; Ras, Verena; Samtal, Chaimae; Tchamga, Milaine S. S.; Sathan, Dassen; Ghoorah, Anisah W.; Sangeda, Raphael Zozimus; Mwita, Liberata; Masamu, Upendo; Kassim, Samar K.; Gill, Zoe; Mungloo-Dilmohamud, Zahra; Wells, Gordon

doi:10.1016/b978-0-12-819803-2.00003-1

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…Previous studies on data sharing have been based on the premise that data holders are willing to participate in data sharing, but, in practice, data holders are often reluctant to share their data when taking into consideration privacy and security issues [8][9][10]. Therefore, to enhance users' willingness to share data, it is necessary not only to study various cryptographic techniques to ensure the realization of secure sharing, but also to design an effective incentive mechanism, to fully promote the flow of data and address the data silo issue [11].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Incentive Mechanism for Smart Grid Data Sharing Based on Evolutionary Game Theory

Zhang,

Lu,

Huang

et al. 2023

Energies

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With the increasing popularization and application of the smart grid, the harm of the data silo issue in the smart grid is more and more prominent. Therefore, it is especially critical to promote data interoperability and sharing in the smart grid. Existing data-sharing schemes generally lack effective incentive mechanisms, and data holders are reluctant to share data due to privacy and security issues. Because of the above issues, a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory is proposed. Firstly, several basic assumptions about the evolutionary game model are given, and the evolutionary game payoff matrix is established. Then, we analyze the stabilization strategy of the evolutionary game based on the payoff matrix, and propose a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory according to the analysis results, aiming to encourage user participation in data sharing. We further write the above evolutionary game model into a smart contract that can be invoked by the two parties involved in data sharing. Finally, several factors affecting the sharing of data between two users are simulated, and the impact of different factors on the evolutionary stabilization strategy is discussed. The simulation results verify the positive or negative incentives of these parameters in the data-sharing game process, and several factors influencing the users’ data sharing are specifically analyzed. This dynamic incentive mechanism scheme for smart grid data sharing based on evolutionary game theory provides new insights into effective incentives for current smart grid data sharing.

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Section: Introductionmentioning

confidence: 99%

A Dynamic Incentive Mechanism for Smart Grid Data Sharing Based on Evolutionary Game Theory

Zhang,

Lu,

Huang

et al. 2023

Energies

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Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review

Majeed

2023

JCP

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Anonymization techniques are widely used to make personal data broadly available for analytics/data-mining purposes while preserving the privacy of the personal information enclosed in it. In the past decades, a substantial number of anonymization techniques were developed based on the famous four privacy models such as k-anonymity, ℓ-diversity, t-closeness, and differential privacy. In recent years, there has been an increasing focus on developing attribute-centric anonymization methods, i.e., methods that exploit the properties of the underlying data to be anonymized to improve privacy, utility, and/or computing overheads. In addition, synthetic data are also widely used to preserve privacy (privacy-enhancing technologies), as well as to meet the growing demand for data. To the best of the authors’ knowledge, none of the previous studies have covered the distinctive features of attribute-centric anonymization methods and synthetic data based developments. To cover this research gap, this paper summarizes the recent state-of-the-art (SOTA) attribute-centric anonymization methods and synthetic data based developments, along with the experimental details. We report various innovative privacy-enhancing technologies that are used to protect the privacy of personal data enclosed in various forms. We discuss the challenges and the way forward in this line of work to effectively preserve both utility and privacy. This is the first work that systematically covers the recent development in attribute-centric and synthetic-data-based privacy-preserving methods and provides a broader overview of the recent developments in the privacy domain.

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Genomics data sharing

Cited by 2 publications

References 54 publications

A Dynamic Incentive Mechanism for Smart Grid Data Sharing Based on Evolutionary Game Theory

A Dynamic Incentive Mechanism for Smart Grid Data Sharing Based on Evolutionary Game Theory

Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review

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