Privacy-preserving formal concept analysis: A homomorphic encryption-based concept construction

TL;DR

This paper introduces a privacy-preserving framework for Formal Concept Analysis that leverages homomorphic encryption to securely construct concepts from sensitive data without compromising privacy.

Contribution

It presents a novel PFCA framework combining binary data and homomorphic encryption for secure, efficient FCA computations on sensitive datasets.

Findings

Effective privacy preservation demonstrated

Maintains computational performance

Secure concept construction validated

Abstract

Formal Concept Analysis (FCA) is extensively used in knowledge extraction, cognitive concept learning, and data mining. However, its computational demands on large-scale datasets often require outsourcing to external computing services, raising concerns about the leakage of sensitive information. To address this challenge, we propose a novel approach to enhance data security and privacy in FCA-based computations. Specifically, we introduce a Privacy-preserving Formal Context Analysis (PFCA) framework that combines binary data representation with homomorphic encryption techniques. This method enables secure and efficient concept construction without revealing private data. Experimental results and security analysis confirm the effectiveness of our approach in preserving privacy while maintaining computational performance. These findings have important implications for privacy-preserving data mining and secure knowledge discovery in large-scale FCA applications.