| 研究実績の概要 |
We have made several technological advances in promoting privacy-preserving, trustworthy, and user-friendly data trading. First, we extended our previous work and proposed a more general pricing method that can prevent data buyers' arbitrage behaviors. Second, we established a novel marketplace for selling training data for machine learning (ML) tasks. In this marketplace, data buyers can purchase utility-optimal ML models with ease according to their task-specific data demands, and data sellers can flexibly control the degree of their privacy leakage. Third, for this marketplace, we further initiated a study on how to fairly, efficiently, and securely evaluate the sellers' contribution to training the models and have obtained some insightful results.
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| 今後の研究の推進方策 |
To incentivize data sellers to contribute valuable data and thus promote data circulation, we will continue investigating novel methods for fair, efficient, and privacy-preserving contribution evaluation in various scenarios of data trading.
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