PrivateSQL: Reimagining and designing a new differentially private SQL query engine

Read the paper co-authored by Tumult Labs founders on building a differentially private relational database system that takes into account the complexity of multi-relational schemas and constraints.

Research

Summary:

‍While differential privacy is considered the dominant standard for private data analysis, much of the research literature applies only to flat tables, ignoring the complexity of multi-relational schemas and constraints. This work presents PrivateSQL, a first-of-its-kind end-to-end differentially private relational database system. We demonstrate that our system can offer error rates 1,000 times lower than competing approaches to answering SQL queries under differential privacy.

‍

other Research articles

Green shape on dark background

Customizing differential privacy to meet legal interpretations of privacy and deliver accurate data

Read the research co-authored by Tumult Labs co-founder Ashwin Machanavajjhala on how to formalize complex privacy requirements mandated by law using novel notions and algorithms based on differential privacy.

Green shape on dark background

PrivateSQL: Reimagining and designing a new differentially private SQL query engine

Read the paper co-authored by Tumult Labs founders on building a differentially private relational database system that takes into account the complexity of multi-relational schemas and constraints.

Green shape on dark background

AIMing Higher: A Smarter Approach to Privacy-Preserving Synthetic Data

Learn how the AIM algorithm, co-invented by Tumult Labs CEO Gerome Miklau, improves upon existing algorithms for synthetic data generation by adapting to the user’s analysis needs and capturing key patterns in the input data.

Green shape on dark background

AIMing Higher: A Smarter Approach to Privacy-Preserving Synthetic Data

Learn how the AIM algorithm, co-invented by Tumult Labs CEO Gerome Miklau, improves upon existing algorithms for synthetic data generation by adapting to the user’s analysis needs and capturing key patterns in the input data.

A Winning Approach to Generating Synthetic Data

A scientific paper, co-authored by our CEO Gerome Miklau, introduces a cutting-edge method for generating differentially private synthetic data.

Evaluating the usability of differential privacy tools with data practitioners

Researchers at University of Vermont ran a usability study to compare various differential privacy tools. Can you guess which platform study participants found easiest to use correctly?

Unleash the power and value of your data.