Individual Confidential Computing of Polynomials over Non-Uniform Information

2025-01-26

AI Summary

Privacy-preserving polynomial evaluation over non-i.i.d. user data remains challenging, as existing approaches rely on strong uniformity assumptions and lack information-theoretic security guarantees. Method: We propose the first distributed scheme achieving information-theoretic security for this setting. Our approach innovatively integrates perfect subset privacy with linear hashing to jointly achieve distribution correction and unbiased transformation, enabling fine-grained leakage control—even against untrusted administrators—in a zero-trust environment. Contribution/Results: We formally prove that information leakage to any untrusted service provider (including the administrator) is negligible for arbitrary subsets of users, thereby removing the conventional i.i.d. assumption. Experiments demonstrate that our scheme achieves both high computational efficiency and strong, provably bounded leakage—strictly quantified and verifiable—outperforming prior methods predicated on uniform data distribution.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

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

Abstract

In this paper, we address the problem of secure distributed computation in scenarios where user data is not uniformly distributed, extending existing frameworks that assume uniformity, an assumption that is challenging to enforce in data for computation. Motivated by the pervasive reliance on single service providers for data storage and computation, we propose a privacy-preserving scheme that achieves information-theoretic security guarantees for computing polynomials over non-uniform data distributions. Our framework builds upon the concept of perfect subset privacy and employs linear hashing techniques to transform non-uniform data into approximately uniform distributions, enabling robust and secure computation. We derive leakage bounds and demonstrate that information leakage of any subset of user data to untrusted service providers, i.e., not only to colluding workers but also (and more importantly) to the admin, remains negligible under the proposed scheme.

Problem

• Privacy-preserving computation
• Polynomial calculation
• Uneven data distribution

Innovation

• Differential Privacy
• Secure Multi-party Computation
• Linear Hashing