# Private, Augmentation-Robust and Task-Agnostic Data Valuation Approach for Data Marketplace

arXiv: https://arxiv.org/abs/2411.00745 · HTML: https://mlsanity.com/paper/2411.00745/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Variational Lossy Autoencoder](https://mlsanity.com/paper/1611.02731/) (2016)
- [Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms](https://mlsanity.com/paper/1908.08619/) (2019)
- [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2205.15466/) (2022)
- [A Distributional Framework for Data Valuation](https://mlsanity.com/paper/2002.12334/) (2020)
- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [LAVA: Data Valuation without Pre-Specified Learning Algorithms](https://mlsanity.com/paper/2305.00054/) (2023)
- [Data Valuation Without Training of a Model](https://mlsanity.com/paper/2301.00930/) (2023)
- [Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards](https://mlsanity.com/paper/2112.09327/) (2021)
- [Fundamentals of Task-Agnostic Data Valuation](https://mlsanity.com/paper/2208.12354/) (2022)
- [Data Measurements for Decentralized Data Markets](https://mlsanity.com/paper/2406.04257/) (2024)

## Built on by (most cited first, 1 of 1)

- [Efficient Decentralized Multi-task Dataset Valuation via Model Merging](https://mlsanity.com/paper/2607.03346/) (2026)

## Abstract (arXiv)

Evaluating datasets in data marketplaces, where the buyer aim to purchase valuable data, is a critical challenge. In this paper, we introduce an innovative task-agnostic data valuation method called PriArTa which is an approach for computing the distance between the distribution of the buyer's existing dataset and the seller's dataset, allowing the buyer to determine how effectively the new data can enhance its dataset. PriArTa is communication-efficient, enabling the buyer to evaluate datasets without needing access to the entire dataset from each seller. Instead, the buyer requests that sellers perform specific preprocessing on their data and then send back the results. Using this information and a scoring metric, the buyer can evaluate the dataset. The preprocessing is designed to allow the buyer to compute the score while preserving the privacy of each seller's dataset, mitigating the risk of information leakage before the purchase. A key feature of PriArTa is its robustness to common data transformations, ensuring consistent value assessment and reducing the risk of purchasing redundant data. The effectiveness of PriArTa is demonstrated through experiments on real-world image datasets, showing its ability to perform privacy-preserving, augmentation-robust data valuation in data marketplaces.