Work

Towards Learning Vector Quantization in the Setting of Homomorphic Encryption

Year: 2025

Type: conference-paper

Abstract: With federated learning scenarios gaining popularity to outsource computational heavy tasks or to increase generalizability of machine learning models, there is also a rise of research in terms of the security and privacy of the respective data used for these tasks.While differential privacy is studied well for Learning Vector Quantization, we want to present steps towards Homomorphic Encryption.In this regard, we will show theoretically how LVQ-1 can be adapted to be compatible with the TFHE encryption scheme and present experimental results.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-47

Authors: Thomas Davies, Ronny Schubert, Mandy Lange-Geisler, Klaus Dohmen, Thomas Villmann

Institutions: Hochschule Mittweida, NEC (Germany), TU Bergakademie Freiberg

Language: English

Topic: Cryptography and Data Security

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: European Social Fund

OpenAlex ID: W4409480472

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