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Multi-client Functional Encryption for Set Intersection with Non-monotonic Access Structures in Federated Learning

Ruyuan Zhang, Jinguang Han

TL;DR

The paper addresses secure, flexible data access in federated learning by introducing MCFE-SI-NAS, a multi-client functional encryption scheme for set intersection with non-monotonic access structures. It proposes a non-interactive, label-bound encryption framework where multiple clients independently encrypt data and an authorized aggregator computes intersections without learning plaintext, resisting mix-and-match attacks. The authors formalize definitions and security models, provide a concrete construction based on asymmetric prime-order bilinear pairings, prove security in the random oracle model, and implement an efficiency analysis using JPBC on Type-III pairings. This work enables fine-grained, non-interactive access control for encrypted data in FL and provides practical guidance on performance and security trade-offs for real-world deployments.

Abstract

Federated learning (FL) based on cloud servers is a distributed machine learning framework that involves an aggregator and multiple clients, which allows multiple clients to collaborate in training a shared model without exchanging data. Considering the confidentiality of training data, several schemes employing functional encryption (FE) have been presented. However, existing schemes cannot express complex access control policies. In this paper, to realize more flexible and fine-grained access control, we propose a multi-client functional encryption scheme for set intersection with non-monotonic access structures (MCFE-SI-NAS), where multiple clients co-exist and encrypt independently without interaction. All ciphertexts are associated with an label, which can resist "mix-and-match" attacks. Aggregator can aggregate ciphertexts, but cannot know anything about the plaintexts. We first formalize the definition and security model for the MCFE-SI-NAS scheme and build a concrete construction based on asymmetric prime-order pairings. The security of our scheme is formally proven. Finally, we implement our MCFE-SI-NAS scheme and provide its efficiency analysis.

Multi-client Functional Encryption for Set Intersection with Non-monotonic Access Structures in Federated Learning

TL;DR

The paper addresses secure, flexible data access in federated learning by introducing MCFE-SI-NAS, a multi-client functional encryption scheme for set intersection with non-monotonic access structures. It proposes a non-interactive, label-bound encryption framework where multiple clients independently encrypt data and an authorized aggregator computes intersections without learning plaintext, resisting mix-and-match attacks. The authors formalize definitions and security models, provide a concrete construction based on asymmetric prime-order bilinear pairings, prove security in the random oracle model, and implement an efficiency analysis using JPBC on Type-III pairings. This work enables fine-grained, non-interactive access control for encrypted data in FL and provides practical guidance on performance and security trade-offs for real-world deployments.

Abstract

Federated learning (FL) based on cloud servers is a distributed machine learning framework that involves an aggregator and multiple clients, which allows multiple clients to collaborate in training a shared model without exchanging data. Considering the confidentiality of training data, several schemes employing functional encryption (FE) have been presented. However, existing schemes cannot express complex access control policies. In this paper, to realize more flexible and fine-grained access control, we propose a multi-client functional encryption scheme for set intersection with non-monotonic access structures (MCFE-SI-NAS), where multiple clients co-exist and encrypt independently without interaction. All ciphertexts are associated with an label, which can resist "mix-and-match" attacks. Aggregator can aggregate ciphertexts, but cannot know anything about the plaintexts. We first formalize the definition and security model for the MCFE-SI-NAS scheme and build a concrete construction based on asymmetric prime-order pairings. The security of our scheme is formally proven. Finally, we implement our MCFE-SI-NAS scheme and provide its efficiency analysis.

Paper Structure

This paper contains 18 sections, 2 theorems, 27 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Functional Encryption
  4. MCFE for Set Intersection
  5. FE for Federated Learning
  6. Our Contributions
  7. Organization
  8. Preliminaries
  9. Bilinear Groups
  10. Complexity Assumptions
  11. Access Structures
  12. Linear Secret-Sharing Schemes
  13. System Model
  14. Security Model
  15. The Construction of Our MCFE-SI-NAS Scheme
  16. ...and 3 more sections

Key Result

Proposition 1

Goyal2006 A vector $\overrightarrow{vec}$ is linearly independent of a series of vectors represented by a matrix $\mathcal{L}$ if and only if there is a vector $\overrightarrow{v}$ satisfying that $\mathcal{L} \cdot \overrightarrow{v} = \overrightarrow{0}$ and $\overrightarrow{vec}\cdot \overrightar

Figures (6)

  • Figure 1: System model of our MCFE-SI-NAS scheme.
  • Figure 2: The Setup algorithm of our MCFE-SI-NAS scheme.
  • Figure 3: The KeyGen algorithm of our MCFE-SI-NAS scheme.
  • Figure 4: The Enc algorithm of the MCFE-SI-NAS scheme.
  • Figure 5: The Dec algorithm of the MCFE-SI-NAS scheme.
  • ...and 1 more figures

Theorems & Definitions (9)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Proposition 1
  • Definition 5
  • Definition 6
  • Theorem 1
  • proof