Sell Data to AI Algorithms Without Revealing It: Secure Data Valuation and Sharing via Homomorphic Encryption

2025-12-04

AI Summary

This paper addresses the “value–privacy dilemma” (Arrow’s information paradox) in data markets: buyers cannot assess a dataset’s utility for model training without accessing it, yet access compromises privacy. To resolve this, we propose the Trusted Impact Protocol (TIP), the first framework integrating homomorphic encryption with gradient-based influence functions to quantify data utility under end-to-end encryption of raw data. We further introduce low-rank gradient projection to enable efficient and secure computation for large language models. Experiments reveal that data value in pretraining corpora follows a heavy-tailed distribution—challenging uniform pricing paradigms. Empirical validation in healthcare and generative AI applications demonstrates that encrypted utility signals strongly correlate with clinical outcomes, achieving accuracy within 1–2% of plaintext baselines.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

The rapid expansion of Artificial Intelligence is hindered by a fundamental friction in data markets: the value-privacy dilemma, where buyers cannot verify a dataset's utility without inspection, yet inspection may expose the data (Arrow's Information Paradox). We resolve this challenge by introducing the Trustworthy Influence Protocol (TIP), a privacy-preserving framework that enables prospective buyers to quantify the utility of external data without ever decrypting the raw assets. By integrating Homomorphic Encryption with gradient-based influence functions, our approach allows for the precise, blinded scoring of data points against a buyer's specific AI model. To ensure scalability for Large Language Models (LLMs), we employ low-rank gradient projections that reduce computational overhead while maintaining near-perfect fidelity to plaintext baselines, as demonstrated across BERT and GPT-2 architectures. Empirical simulations in healthcare and generative AI domains validate the framework's economic potential: we show that encrypted valuation signals achieve a high correlation with realized clinical utility and reveal a heavy-tailed distribution of data value in pre-training corpora where a minority of texts drive capability while the majority degrades it. These findings challenge prevailing flat-rate compensation models and offer a scalable technical foundation for a meritocratic, secure data economy.

Problem

• Enables secure data valuation without revealing raw data
• Resolves privacy-utility dilemma in AI data markets
• Scales encrypted valuation for large language models efficiently

Innovation

• Homomorphic encryption for secure data valuation
• Gradient-based influence functions for blinded scoring
• Low-rank projections for scalable LLM evaluation