The results of this work have significant implications for the design of privacy-preserving data markets. By demonstrating that public data can be effectively utilized to improve the accuracy of differentially private models, this research suggests that data markets could incentivize individuals to share their data while ensuring their privacy. The ability to harness public data alongside private data can lead to more accurate models, which in turn can enhance the value of the data being sold.
Moreover, the findings indicate that individuals could benefit from selling their data in a manner that is compatible with differential privacy, potentially leading to better compensation for their contributions. Data markets could implement mechanisms that allow individuals to control the level of privacy they wish to maintain while still enabling the use of their data for model training. This could involve providing users with options to choose the degree of noise added to their data or the extent to which their data is aggregated with public datasets.
However, it is crucial to ensure that the design of these markets includes robust privacy guarantees to prevent misuse of the data. The results highlight the need for transparency in how public and private data are combined and the importance of maintaining trust between data providers and consumers. Overall, the insights from this work can guide the development of ethical frameworks and technical standards for privacy-preserving data markets.