PEPR '25: 2025 USENIX Conference on Privacy Engineering Practice and Respect

Daniele Romanini, Resolve


In today's data-driven landscape, organizations often seek collaborative analytics to gain cross-organizational insights while upholding stringent privacy standards. This talk introduces a practical approach to adopting a Secure Multi-Party Computation (MPC) system for cloud-based data analytics. Leveraging open-source frameworks such as Carbyne Stack and MP-SPDZ, we have developed features to enable non-cryptographic and non-MPC expert developers to perform private analytics using intuitive, Python-like code. In this talk, we focus on the practical features that a real-world MPC solutions should have, presenting lessons learned and the key modification to an existing framework to reach a stable deployment.

We explain how we enhanced usability and functionalities of the existing framework (Carbyne Stack with MP-SPDZ), such as implementing the support for a semi-honest security model, which is less expensive and more practical than a malicious one in some real-world settings. We also address practical considerations of cost and performance, presenting strategies to optimize infrastructure deployment and algorithm-level enhancements to improve costs and enable complex analytics. Moreover, we illustrate a practical example on how the platform can be leveraged in the AdTech world. This presentation aims to demonstrate that secure and efficient cross-organizational data analytics are achievable, even for developers without specialized MPC expertise.

Authors: Adrián Vaca Humanes, Gerardo González Seco, Daniele Romanini, Goran Stipcich


https://www.usenix.org/conference/pepr25/presentation/romanini-unlocking