Non-Rival Data as Rival Products: An Encapsulation-Forging Approach for Data Synthesis

2025-11-10

AI Summary

The non-rivalrous nature of data creates a fundamental tension between shared value and competitive advantage: existing synthetic data methods produce symmetric utility, exacerbating value leakage. To address this, we propose the “Encapsulate–Forge” framework—the first to endow synthetic data with *exclusive utility*. It encapsulates knowledge from raw data into a designated “key” model and generates synthetic data that overfits *only* that model, thereby binding data value strictly to the authorized model. This mechanism breaks utility symmetry, enabling asymmetric value allocation while preserving strong privacy guarantees and robustness against misuse. Empirically, it achieves original-data-level performance using significantly smaller synthetic datasets. Our core contribution is transforming non-rivalrous data into a *model-level excludable*, rivalrous product—establishing a new paradigm for secure, controllable data collaboration.

Technology Category

• Machine Learning: Privacy
• Game Theory and Economic Paradigms: Adversarial Learning
• Computer Vision: Diffusion Models for Vision

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The non-rival nature of data creates a dilemma for firms: sharing data unlocks value but risks eroding competitive advantage. Existing data synthesis methods often exacerbate this problem by creating data with symmetric utility, allowing any party to extract its value. This paper introduces the Encapsulation-Forging (EnFo) framework, a novel approach to generate rival synthetic data with asymmetric utility. EnFo operates in two stages: it first encapsulates predictive knowledge from the original data into a designated ``key''model, and then forges a synthetic dataset by optimizing the data to intentionally overfit this key model. This process transforms non-rival data into a rival product, ensuring its value is accessible only to the intended model, thereby preventing unauthorized use and preserving the data owner's competitive edge. Our framework demonstrates remarkable sample efficiency, matching the original data's performance with a fraction of its size, while providing robust privacy protection and resistance to misuse. EnFo offers a practical solution for firms to collaborate strategically without compromising their core analytical advantage.

Problem

• Preventing competitive erosion from data sharing
• Creating synthetic data with asymmetric utility
• Preserving data owners' analytical advantage

Innovation

• Generates synthetic data with asymmetric utility
• Encapsulates knowledge into a key model first
• Optimizes data to overfit the key model