  Federated Learning (FL) has garnered significant attention for its potential
to protect user privacy while enhancing model training efficiency. For that
reason, FL has found its use in various domains, from healthcare to industrial
engineering, especially where data cannot be easily exchanged due to sensitive
information or privacy laws. However, recent research has demonstrated that FL
protocols can be easily compromised by active reconstruction attacks executed
by dishonest servers. These attacks involve the malicious modification of
global model parameters, allowing the server to obtain a verbatim copy of
users' private data by inverting their gradient updates. Tackling this class of
attack remains a crucial challenge due to the strong threat model. In this
paper, we propose a defense mechanism, namely OASIS, based on image
augmentation that effectively counteracts active reconstruction attacks while
preserving model performance. We first uncover the core principle of gradient
inversion that enables these attacks and theoretically identify the main
conditions by which the defense can be robust regardless of the attack
strategies. We then construct our defense with image augmentation showing that
it can undermine the attack principle. Comprehensive evaluations demonstrate
the efficacy of the defense mechanism highlighting its feasibility as a
solution.
