2022 Fiscal Year Annual Research Report
Fundamental Technologies for Machine Learning Centric Data Trading
| Project/Area Number |
21J23090
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| Allocation Type | Single-year Grants |
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| Research Institution | Kyoto University |
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Principal Investigator |
鄭 舒元 京都大学, 情報学研究科, 特別研究員(DC1)
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| Project Period (FY) |
2021-04-28 – 2024-03-31
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| Keywords | data market / privacy protection / data valuation |
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| Outline of Annual Research Achievements |
We have made a significant advance in building a reliable data market for machine learning (ML) applications. Concretely, in a collaborative data marketplace where multiple data owners collaborate to train an ML model, it is essential to evaluate the owners' diverse contributions to the model's utility to encourage fair cooperation. However, existing studies have neglected the potential privacy leakage in the contribution evaluation process. We have proposed pioneering methods for privacy-preserving contribution evaluation in collaborative ML to address this significant limitation. Our methods enable buyers to estimate data products' qualities before purchasing without accessing the products and sacrificing data owners' privacy, which considerably promotes reliable data trading.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We have completed this fiscal year's research plan as scheduled with high quality. We designed a fair, efficient, and privacy-preserving method for evaluating contributions in data trading, which we have published in the top-tier journal PVLDB. Furthermore, we presented the work accomplished in the previous year at the prestigious international conference IEEE BigData 2022.
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| Strategy for Future Research Activity |
We will initiate a research task on how to prevent data buyers from stealing ML models from prediction API marketplaces. Firstly, we need to investigate and characterize potential adversaries who may attempt model stealing attacks in the prediction API market and define our goals to formulate our research problem. Then, we plan to tackle this challenging problem using a game-theoretical approach. Solving this problem will have significant positive implications for building a reliable and sustainable data marketplace.
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