Shuffled Check-in: Privacy Amplification towards Practical Distributed Learning

Recent studies of distributed computation with formal privacy guarantees, such as differentially private (DP) federated learning, leverage random sampling of clients in each round (privacy amplification by subsampling) to achieve satisfactory levels of privacy. Achieving this however requires strong assumptions which may not hold in practice, including precise and uniform subsampling of clients, and a highly trusted aggregator to process clients' data. In this paper, we explore a more practical protocol, shuffled check-in, to resolve the aforementioned issues. The protocol relies on client making independent and random decision to participate in the computation, freeing the requirement of server-initiated subsampling, and enabling robust modelling of client dropouts. Moreover, a weaker trust model known as the shuffle model is employed instead of using a trusted aggregator. To this end, we introduce new tools to characterize the R\'enyi differential privacy (RDP) of shuffled check-in. We show that our new techniques improve at least three times in privacy guarantee over those using approximate DP's strong composition at various parameter regimes. Furthermore, we provide a numerical approach to track the privacy of generic shuffled check-in mechanism including distributed stochastic gradient descent (SGD) with Gaussian mechanism. To the best of our knowledge, this is also the first evaluation of Gaussian mechanism within the local/shuffle model under the distributed setting in the literature, which can be of independent interest.

MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI

The advance of explainable artificial intelligence, which provides reasons for its predictions, is expected to accelerate the use of deep neural networks in the real world like Machine Learning as a Service (MLaaS) that returns predictions on queried data with the trained model. Deep neural networks deployed in MLaaS face the threat of model extraction attacks. A model extraction attack is an attack to violate intellectual property and privacy in which an adversary steals trained models in a cloud using only their predictions. In particular, a data-free model extraction attack has been proposed recently and is more critical. In this attack, an adversary uses a generative model instead of preparing input data. The feasibility of this attack, however, needs to be studied since it requires more queries than that with surrogate datasets. In this paper, we propose MEGEX, a data-free model extraction attack against a gradient-based explainable AI. In this method, an adversary uses the explanations to train the generative model and reduces the number of queries to steal the model. Our experiments show that our proposed method reconstructs high-accuracy models -- 0.97$\times$ and 0.98$\times$ the victim model accuracy on SVHN and CIFAR-10 datasets given 2M and 20M queries, respectively. This implies that there is a trade-off between the interpretability of models and the difficulty of stealing them.