A Secure Multiparty Computation Platform for Squeaky-Clean Data Rooms

Abstract

Modern approaches for multiparty secure collaboration must strike the right balance between rich analytics and requisite data privacy guarantees, especially in the face of new regulations. While cryptographic technologies such as fully homomorphic encryption (FHE) and secure multiparty computation (MPC) provide strong, provable security guarantees as standalone tools, deploying them in practice throws up a myriad of challenges, including usability constraints and lack of precise specification of privacy guarantees. In this work, we propose a novel framework for real-world deployment of cryptographic privacy preserving techniques that achieves the twin goals of practical usability in real-world setting and provable privacy guarantees from users' perspective. To this end, we formalize the notion of a secure computation platform (SCP) for privacy preserving data collaboration, and introduce a model for precise specification of privacy guarantees for multiparty workflows. We then describe abstractions of a set of cryptoprimitives, that are usable by non-experts in cryptography. We present two demo workflows that empirically validate our claims, and serve as potential building blocks for the development of squeaky-clean data rooms with practical performance and privacy guarantees.