Exploiting Internal Randomness for Privacy in Vertical Federated Learning

Vertical Federated Learning (VFL) is becoming a standard collaborative learning paradigm with various practical applications. Randomness is essential to enhancing privacy in VFL, but introducing too much external randomness often leads to an intolerable performance loss. Instead, as it was demonstrated for other federated learning settings, leveraging internal randomness – as provided by variational autoencoders (VAEs) –can be beneficial. However, the resulting privacy has never been quantified so far, nor has the approach been investigated for VFL. We therefore propose a novel differential privacy (DP) estimate, denoted as distance-based empirical local differential privacy ( \(\textsf{dELDP}\) ). It allows us to empirically bound DP parameters of models or model components, quantifying the internal randomness with appropriate distance and sensitivity metrics. We apply \(\textsf{dELDP}\) to investigate the DP of VAEs and observe values up to \(\epsilon \approx 6.4\) and \(\delta = 2^{-32}\) . Based on this, to link the \(\textsf{dELDP}\) parameters to the privacy of VAE-including VFL systems in practice, we conduct comprehensive experiments on the robustness against state-of-the-art privacy attacks. The results illustrate that the VAE system is robust against feature reconstruction attacks and outperforms other privacy-enhancing methods for VFL, especially when the adversary holds 75% of the features during label inference attacks.