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Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in realworld datasets and systems remains challenging.

Tumult Analytics is an open-source framework for releasing aggregate information from sensitive datasets with differential privacy (DP).

When the U.S. Census Bureau needed to release data on the median earnings of post-secondary graduates, one Tumult Lab founder helped the Census Bureau design a new differentially private algorithm.

We are proud to be named a strategic partner in Google’s latest initiative to make BigQuery data clean rooms available in public preview, announced last week at NEXT ‘23.

Damien Desfontaines describes how differential privacy can bring together open data with safe, privacy-preserving publication practices.

In this blog post, we review legacy techniques used to anonymize data. We give real-world examples of the failure modes of these techniques. Then, we draw some lessons from these historical failures.

According to Gartner, “Business data holds value and much of it is personal data. Regulations that constrain the use of personal data are increasing, and the liability for misusing personal data can be substantial.

In this article, we first explain what it means for differential privacy software to have vulnerabilities.

Today, we’re excited to share what we’ve been working on for a few years: Tumult Analytics, a Python library making it easy and safe to use differential privacy, is now open-source!

For the first time, anyone interested in better understanding Wikipedia page view data at a more granular, country-specific level can find regularly published data and useful insights.

‍While differential privacy offers robust privacy protection, it applies noise to statistics in a manner that can sometimes unfairly impact certain groups.

Say you’re working on a new project involving sensitive data — for example, adding a new feature to a healthcare app.

Suppose you work on the data analytics team of a social media company. You want to share insights about user interactions on your company’s new app, while protecting each user’s data.

‍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.

Using data clean room technology to join data between two parties does not necessarily mean that the data is fully protected — the output of the process can sometimes leak individual information.

Some data publication or sharing use cases are well-suited to the use of differential privacy, while some aren’t.

Are you a Google Cloud customer who wants to safely share or publish insights from sensitive data? We have good news for you.

‍Organizations that collect highly personal, individual-level data, like statistical agencies and medical institutions, rely on differential privacy algorithms for safe, reliable summaries.

Much of the research into privacy preserving data analytics is focused on the tradeoff between privacy and utility/accuracy.

‍While differential privacy has emerged as the leading solution for achieving statistically sound, private summaries, designing custom private, efficient, and accurate algorithms is still very complex.

Tumult designed a novel differentially-private algorithm that the US Census Bureau is considering for use in the release the Detailed Demographic and Housing Characteristics (DHC) Race & Ethnicity tabulations as part of the 2020 Census.

National statistical agencies (like those that work for the U.S. Census Bureau) need to be able to publish summaries based on combined employer-employee data.

A synthetic dataset consists of a collection of records which are generated to match the properties of an original data source.

Curious to see the best-in-class differential privacy framework in action?