Data is power, and companies today rely on it more than ever. But as data fuels opportunity, it also fuels risk without proper privacy and security.

Brands on both sides of the pond can look at Meta’s misstep and take the initiative to make sure they don’t follow suit.

Data Clean Rooms are not the panacea they appear to be.

This blog post covers an overview of Federated Learning techniques and their comparison to Secure Multiparty Computation (SMPC). While we don't go into technical details, we do survey the problems with existing Federated Learning implementations.

Learn about the importance of "data clean rooms" - a solution that allows for private data sharing and analysis - in this blog post. We discuss the trade-offs involved in choosing between generic third-party solutions and custom open-source options, and how open-source SMC frameworks provide building blocks for custom clean rooms. With open SMC technologies and the right team, any company can achieve a strategic advantage through proprietary, self-governed data analysis and sharing systems that move as fast as - or faster than - vendor options ever will. Join us in exploring the future of data privacy and innovation on companies' terms.

Homomorphic encryption (HE) is a privacy technology that enables computation on encrypted data. This post covers the different types of HE, compares HE to secure multi-party computation (SMPC), and discusses the benefits and limitations of HE. The post also provides guidance on when to consider using HE for a specific use case.