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

Tayyebeh Jahani-Nezhad, Parsa Moradi, Mohammad Ali Maddah-Ali, Giuseppe Caire

TL;DR

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.

Abstract

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.

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

TL;DR

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.

Abstract

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.

Paper Structure

This paper contains 13 sections, 1 theorem, 15 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Our Contribution
  4. Building Blocks of $\mathsf{PriArTa}$
  5. Mod.1: Unsupervised Representation Learning
  6. Mod.2: Distribution Mapping
  7. Mod.3: Valuation Metric
  8. Mod.4: Privacy-Preserving Mechanism
  9. Detailed Description of $\mathsf{PriArTa}$
  10. Experiments and Results
  11. Implementation Details
  12. Conclusion
  13. Sensitivity of Mean and Covariance Functions

Key Result

Theorem 1

For $\epsilon,\delta\in(0,1)$, the Gaussian mechanism with parameter $\sigma\ge c\Delta/\epsilon$ is $(\epsilon,\delta)$-differentially private, provided that $c^2>2\ln{(1.25/\delta)}$dwork2014algorithmicdwork2006calibrating.

Figures (4)

  • Figure 4: The probability distribution of datasets based on their respective classes. (a) represents the distribution of the buyer's dataset, while (b) and (c) represent distributions for seller-1 and seller-2 respectively.
  • Figure 5: Sample images of the buyer's dataset and four sellers'.
  • Figure 6: The valuation score of different sellers' datasets, where the underlying datasets are CIFAR-10 and STL-10.
  • Figure 7: Performance improvement of the buyer's model with seller datasets.

Theorems & Definitions (2)

  • Definition 1
  • Theorem 1: Gaussian Mechanism