"Wasserstein Markets for Differentially-Private Data"

A marketplace for private data that actually works - using math to balance privacy and value.

Your data is valuable, but how much? This paper cracks the code using Wasserstein metrics, and a way to sell your data without compromising privacy or getting ripped off

This paper introduces a novel data market framework using Wasserstein distance to value and trade differentially-private data, enabling privacy-preserving data sharing while determining optimal privacy-utility tradeoffs.

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https://arxiv.org/abs/2412.02609v1

🤔 Original Problem:

Data markets struggle with two key challenges: accurately valuing data while preserving privacy, and determining fair compensation for data owners. Existing solutions either need trusted third parties or can't properly capture data's combinatorial value.

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💡 Solution in this Paper:

→ The paper proposes a valuation mechanism based on Wasserstein distance for differentially-private data.

→ It develops three procurement mechanisms: a budget-feasible mechanism for task-agnostic data, an endogenous budget mechanism, and a joint optimization mechanism.

→ The solution uses mixed-integer second-order cone programming to make these mechanisms computationally tractable.

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🔑 Key Insights:

→ Wasserstein distance provides better data valuation metrics compared to other statistical distances

→ Privacy-preserving computation can be achieved without sharing raw datasets

→ The framework captures both task-specific and task-agnostic data procurement scenarios

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📊 Results:

→ Successfully validated using numerical studies with synthetic data

→ Demonstrated practical feasibility through reformulation as tractable mixed-integer programs

→ Proved theoretical bounds on performance guarantees for data-driven decision making