Protecting our private information from the prying eyes of other people, governments, and corporations is on many people’s minds. At the same time, our lack of control over our own data makes us wonder if privacy even matters in this day and age. In this article, I’ll argue that protecting our Personally Identifiable Information (PII) …
New techniques in analytics and machine learning offer the ability to process ever-increasing amounts of data, but access to such data has lagged far behind the technological advances in data processing. High-value data is often split across multiple organizations and access to it is encumbered by business competition and regulatory constraints. For example, banks wish …
Federated Learning vs. Secure Collaborative Learning Read More »
This is part 2 of a two part blog post on our migration to the polyrepo. If you haven’t yet read part 1 and want some more context, you can find it here. In part 1 of this blog post, we discussed how our original polyrepo led to complicated workflows which hindered engineering productivity, why …
Introduction Our team spent years at UC Berkeley actively building open source software to support our published research. The open source software, the MC2 Project, is a platform consisting of various libraries and packages that enable secure collaborative analytics and machine learning. While building the platform we created a repository (repo) for each paper; as …
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 data in use remains …
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 untrusted sections to improve runtime …
Secure computation has become increasingly popular for protecting the privacy and integrity of data during computation. The reason is that it provides two tremendous advantages. The first advantage is that it offers “encryption in use” in addition to the already existing “encryption at rest” and “encryption in transit”. The “encryption in use” paradigm is important …
Secure Computation: Homomorphic Encryption or Hardware Enclaves? Read More »
Opaque Systems provides solutions that make it easier for enterprises to analyze confidential data through secure means, unlocking a $3 trillion market SAN FRANCISCO–(BUSINESS WIRE) — July 7, 2021: Opaque Systems, the secure data analytics platform company, today announced $9.5M in seed funding, led by Intel Capital with participation by Race Capital, The House Fund, …
