Solutions for Advertising start with the abilityto share data safely

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Enable data-driven product and campaigndecision-making and reporting whileprotecting individual privacy.

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use cases

Our solutions and platform improve user engagement and enable data-driven campaign decision-making and reporting,  while protecting individual privacy.

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our process

A collaborative, calibrated process to assure utility while maintaining privacy

Proven with data-driven leaders, our process and platform deliver on your specific goals.

Define the problem
Outline the problem, explaining the data release purpose, strategy, privacy considerations, error metrics, and a pseudocode algorithm draft.
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Confirm the viability of using DP
Using default hyperparameters, see if it is possible to conduct a differentially- private data aggregation.
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Decide on error metrics to optimize for
Develop internal error metrics to assess the utility of the differentially-private dataset, considering that while added noise is necessary for privacy, excessive noise can compromise data usefulness.
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Experiment with a wide variety of hyperparameters
Before finalizing error metrics, conduct a grid search on hyperparameters such as output threshold, noise scale, and keyset to find the optimal set using the predefined error metrics.
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Productionize the pipeline
Finalize the script with integrated error calculation and privacy loss logging, then automate its execution to run at regular intervals.

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Leading data-focused enterprises work with Tumult

How we use differential privacy

Derive valuable insights and boost monetization of sensitive data, while ensuring privacy preservation.

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Raw personal data: too sensitive to use
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Protective transformation, with DP
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Safe to share summary data. DP offers valuable insights with guaranteed privacy.

Proven in production, Tumult's solutions for advertising and publishing advance the work of the world's largest social web properties.

Improve user engagement, and enable data-driven campaign decision-making and reporting,  while protecting individual privacy.

Solutions for publishing and advertising

Why is differential privacy an ideal fit for advertising and publishing’s data use cases?

The advertising and publishing industries are particularly reliant on user data for targeting, personalization, and measurement. DP supports enterprises’ ability to use data while complying with growing concerns and regulations around user privacy and data risk.

Adopting differential privacy not only enhances privacy protection and regulatory compliance but supports innovation that can result from internal and external data sharing, and competitive differentiation around privacy-protecting brand values.

Leading online publishers use differential privacy to improve user engagement, and enable data-driven campaign decision-making and reporting,  while protecting individual privacy.

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Research

Evaluating the usability of differential privacy tools with data practitioners

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

White paper

Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy

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

Research

Using differential privacy to safely release earnings data on post-secondary grads

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.

Research

Evaluating the usability of differential privacy tools with data practitioners

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

White paper

Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy

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

Research

Using differential privacy to safely release earnings data on post-secondary grads

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.

Unleash the power and value of your data.

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