Summary of Private, Augmentation-robust and Task-agnostic Data Valuation Approach For Data Marketplace, by Tayyebeh Jahani-nezhad et al.
Private, Augmentation-Robust and Task-Agnostic Data Valuation Approach for Data Marketplace
by Tayyebeh Jahani-Nezhad, Parsa Moradi, Mohammad Ali Maddah-Ali, Giuseppe Caire
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents PriArTa, a novel task-agnostic data valuation method for evaluating datasets in data marketplaces. The goal is to determine how effectively new data can enhance the buyer’s existing dataset without requiring access to the entire seller’s dataset. PriArTa calculates the distance between the two distributions using communication-efficient preprocessing and scoring metrics. This approach ensures privacy preservation and robustness to common data transformations, reducing the risk of redundant data purchases. The method is demonstrated on real-world image datasets, showcasing its ability to perform privacy-preserving and augmentation-robust data valuation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine buying data online, but not knowing if it’s really valuable or useful for your needs. This paper introduces a new way to figure out how good a dataset is without having to see the whole thing. It’s called PriArTa, and it helps you compare datasets by looking at how they’re similar or different from what you already have. This method is special because it keeps each seller’s data private and makes sure the results are consistent, even if the data is changed in some way. The paper shows that this approach works well on real images, so it could be useful for people buying data online. |
