2023 Fiscal Year Annual Research Report
Fundamental Technologies for Machine Learning Centric Data Trading
| Project/Area Number |
22KJ1721
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| Allocation Type | Multi-year Fund |
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| Research Institution | Osaka University |
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Principal Investigator |
鄭 舒元 大阪大学, 情報科学研究科, 特任助教
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Keywords | data trading / data protection / GDPR / computer simulation / large language model |
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| Outline of Annual Research Achievements |
Our contributions for this fiscal year are twofold. First, we conducted an interdisciplinary study on data protection in data markets. This study discusses the ambiguous boundaries among different categories of user data as defined in the GDPR from a legal perspective and proposes a computational method to delineate these boundaries clearly. Second, we developed a computer simulation framework to simulate data trading in practice. This framework employs large language model agents to represent the various parties in data markets and to simulate their interactions during data trading. Based on the simulation results, we can more accurately assess the performance of data trading mechanisms in society, thereby fostering trustworthy data trading.
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