Federated Learning (FL) enables multiple clients to collaboratively train a machine learning model while keeping their data private, eliminating the need for data sharing. Two common approaches to secure aggregation (SA) in FL are the single-aggregator and multiple-aggregator models. This work focuses on improving the multiple-aggregator model.Existing multiple-aggregator protocols such as Prio (NSDI 2017), Prio+ (SCN 2022), Elsa (S&P 2023) either offer robustness only in the presence of semi-honest servers or provide security without robustness and are limited to two aggregators. We introduce Mario, the first multiple-aggregator Secure Aggregation protocol that is both secure and robust in a malicious setting. Similar to prior work of Prio and Prio+, Mario provides secure aggregation in a setup of n servers and m clients. Unlike previous work, Mario removes the assumption of semi-honest servers, and provides a complete protocol with robustness under malicious clients and malicious servers. Our implementation shows that Mario is 3.40× and 283.4× faster than Elsa and Prio+, respecitively.

Generative models have achieved remarkable success in a wide range of applications. Training such models using proprietary data from multiple parties has been studied in the realm of federated learning. Yet recent studies showed that reconstruction of authentic training data can be achieved in such settings. On the other hand, multiparty computation (MPC) guarantees standard data privacy, yet scales poorly for training generative models. In this paper, we focus on improving reconstruction hardness during Generative Adversarial Network (GAN) training while keeping the training cost tractable. To this end, we explore two training protocols that use a public generator and an MPC discriminator: Protocol 1 (P1) uses a fully private discriminator, while Protocol 2 (P2) privatizes the first three discriminator layers. We prove reconstruction hardness for P1 and P2 by showing that (1) a public generator does not allow recov- ery of authentic training data, as long as the first two layers of the discriminator are private; and through an existing approximation hardness result on ReLU networks, (2) a discriminator with at least three private layers does not allow authentic data reconstruction with algorithms polynomial in network depth and size. We show empirically that compared with fully MPC training, P1 reduces the training time by 2× and P2 further by 4 − 16×. Our implementation can be found at https://github.com/asu-crypto/ppgan.

Researchers across various fields seek to understand causal relationships but often find controlled experiments impractical. To address this, statistical tools for causal discovery from naturally observed data have become crucial. Non-linear regression models, such as Gaussian process regression, are commonly used in causal inference but have limitations due to high costs when adapted for secure computation. Support vector regression (SVR) offers an alternative but remains costly in an Multi-party computation context due to conditional branches and support vector updates.