Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation
Revolutionizing Data Valuation for Secure AI Collaboration
This paper introduces a novel privacy-preserving implementation of Shapley-CMI for Vertical Federated Learning (VFL), addressing the critical challenge of evaluating feature contributions without exposing raw data or requiring a pre-trained model. It leverages a private set intersection (PSI) server to securely handle feature permutations and compute encrypted intersection sizes, allowing parties to locally derive feature valuation.
Key Advantages for Enterprise AI
Our analysis reveals the transformative impact of this privacy-preserving approach on collaborative AI initiatives, enabling secure and fair data valuation without compromising sensitive information.
0%
Privacy Assurance
0%
Accuracy Equivalence
Yes
Model-Free Valuation
High
Scalability Across Parties
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Vertical Federated Learning (VFL) is crucial for organizations to collaborate on AI models using complementary datasets without sharing sensitive raw data. A major hurdle is upfront feature valuation, especially before a model is built. This paper addresses this by proposing a privacy-preserving Shapley-CMI method. This allows parties to understand the value of their data contributions, enabling fair compensation and identifying redundant features, all while maintaining strict data privacy.
Identical Results
Between original and encrypted Shapley-CMI implementations
Fraud Detection Collaboration
A bank and an e-commerce platform collaborate using VFL to build a fraud detection model. The bank contributes features like income and loan history, while the e-commerce platform provides spending patterns and purchase frequency. This approach allows both institutions to enhance their fraud detection capabilities without direct data sharing, respecting stringent privacy regulations.
"VFL is essential for enabling institutions that cannot share raw data, but need data owned by other institutions to train a collaborative model."
— Introduction, Page 1
The core of the system lies in its secure implementation of Shapley-CMI. Parties first encrypt user IDs with HMAC-SHA256 and discretize feature values into 'ID groups'. These encrypted groups are sent to a PSI server, which generates random feature permutations and calculates encrypted intersection sizes for various value combinations. Critically, the PSI server never sees raw data or decrypts IDs. The intersection sizes are then returned to the parties, who compute their Shapley-CMI values locally.
Enterprise Process Flow
Parties Encrypt & Discretize Data
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Send Encrypted ID Groups to PSI Server
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PSI Server Computes Permutations & Intersections
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PSI Server Sends Intersection Sizes to Parties
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Parties Compute Shapley-CMI Locally
| Feature | Previous Methods | Proposed System |
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| Model Dependency |
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| Raw Data Sharing |
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| Security |
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Experimental validation compared the proposed encrypted Shapley-CMI method with the original method and SHAP values from a Random Forest model. The results demonstrated that the encrypted method yielded 'identical' feature importance values to the original Shapley-CMI, confirming its correctness and privacy. While Shapley-CMI and SHAP values differed slightly due to their inherent nature (model-free vs. model-dependent), the overall ranking of feature importance was consistent. The system is highly scalable and reduces computational iterations for the PSI server.
Identical Results
Between original and encrypted Shapley-CMI implementations
Calculate Your Potential AI Impact
Estimate the potential efficiency gains and cost savings for your organization by leveraging advanced AI solutions.
Annual Cost Savings
$0
Annual Hours Reclaimed
0
Your Path to Secure AI Collaboration
We've outlined a strategic roadmap to guide your enterprise in adopting secure, privacy-preserving AI solutions based on the insights from this research.
Enhanced Privacy Techniques (3-6 Months)
Explore and integrate alternative hashing algorithms, homomorphic encryption, or other advanced privacy-preserving methods for data handling without decryption.
Adversarial Resilience & Trust (6-12 Months)
Implement robust defenses against malicious PSI server behavior, including distributed or verifiable PSI protocols to reduce reliance on a single central authority.
Wider Adoption & Integration (12-18 Months)
Integrate the Shapley-CMI framework with existing VFL platforms and conduct real-world pilot programs to validate its effectiveness in diverse enterprise settings.
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