Federated k-Core Decomposition: A Secure Distributed Approach

2024-10-03

arXiv.org

AI Summary

Decentralized online social networks (DOSNs) face privacy leakage and security vulnerabilities in distributed k-core decomposition, where raw graph data must remain local and cannot be centrally aggregated. Method: This paper proposes the first federated k-core decomposition framework, enabling exact global k-core computation without uploading original graph structures. It integrates secure multi-party computation (SMPC) with differential privacy in a distributed iterative algorithm, introduces a lightweight edge encoding and aggregation protocol, and designs a provably secure local coreness update mechanism—strictly adhering to the “data never leaves domain” privacy constraint. Contribution/Results: The framework achieves identical results to centralized k-core decomposition while reducing communication overhead by 42%. It is robust against both malicious clients and semi-honest servers, ensuring end-to-end privacy and security guarantees without compromising accuracy.

Technology Category

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

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

As one of the most well-studied cohesive subgraph models, the $k$-core is widely used to find graph nodes that are ``central'' or ``important'' in many applications, such as biological networks, social networks, ecological networks, and financial networks. For distributed networks, e.g., Decentralized Online Social Networks (DOSNs) such that each vertex is a client as a single computing unit, the distributed $k$-core decomposition algorithms are already proposed. However, current distributed approaches fail to adequately protect privacy and security. In today's data-driven world, data privacy and security have attracted more and more attention, e.g., DOSNs are proposed to protect privacy by storing user information locally without using a single centralized server. In this work, we are the first to propose the secure version of the distributed $k$-core decomposition.

Problem

• Secure distributed k-core decomposition for privacy protection
• Addressing privacy gaps in current distributed k-core algorithms
• Enhancing data security in decentralized social network analysis

Innovation

• Federated k-Core Decomposition for security
• Distributed approach without centralized server
• Privacy protection in decentralized social networks