After years of building and shipping products in various areas of the tech industry, I decided to join a company that set out to enable the widespread adoption of strong privacy measures and make a real impact in the world. With so much of our personal data now being stored and processed by any number …
Summary: In this engineering blog post, we discuss technical details surrounding Opaque Systems’ closed source version of Opaque SQL. This project extends Apache Spark SQL with a physical operator layer that runs inside hardware enclaves to protect confidential data in use. However, this latest iteration contains physical operators that are vectorized and are being performed …
Accelerating Encrypted Analytics on Confidential Data by 20x Read More »
We are excited to announce the initial release of the MC² Project, a collection of open source tools for computing and collaborating on confidential data. Developed at UC Berkeley’s RISELab, MC² (Multi-Party Collaboration and Coopetition) enables rich analytics and machine learning on encrypted data, ensuring that data remains concealed even when it’s being processed. The …
TL;DR: We are excited to present Opaque SQL, an open-source platform for securely running Spark SQL queries on encrypted data-in-use. Originally built by top systems and security researchers at UC Berkeley, the platform uses hardware enclaves to securely execute queries on private data in an untrusted environment. Opaque SQL partitions the codebase into trusted and …
Secure computation has become increasingly popular for protecting the privacy and integrity of data during computation. It offers two tremendous advantages: “encryption in use” in addition to the already existing “encryption at rest” and “encryption in transit”, and enabling different parties to collaborate and gain insights from their aggregate data, without sharing their data with …
Secure computation: Homomorphic encryption or hardware enclaves? Read More »
