Solutions for Banking and Finance  start with the ability to share data safely

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Enable data to be shared forvaluable insights, without riskingprivacy or competitiveness

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use cases

Our solutions and platform enable the banking and finance industry to use data more extensively, and for greater insight, while protecting individual privacy and competitiveness.

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our process

A collaborative, calibrated process to assure utility while maintaining privacy

Proven with data-driven leaders in sensitive data-focused industries like banking and finance, 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 insights and inform decisions in credit, finance, and product development from sensitive data, while preserving privacy.

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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 banking and finance advance financial innovation.

Solutions for banking and finance

Why is differential privacy an ideal fit for banking and finance’s data use cases?

The sector’s  unique need for balance between the need for stringent privacy measures and the potential benefits of data analytics and sharing is well matched by DP technology.
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Differential privacy offers the sector a powerful tool to leverage data for innovation, compliance, and improved customer service while upholding the highest standards of privacy protection. This approach not only mitigates risks associated with data sharing and analysis but also aligns with growing regulatory and consumer demands for stringent data privacy.

Videos

Tumult Tune: 5-minute teaser demo

Watch a teaser demo of Tumult Tune, an upcoming product that allows you to easily understand and optimize differentially private data products.
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WEBINARS

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Research

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.

Research

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.

Blog Post

Publishing Wikipedia usage data with strong privacy guarantees

Tumult Labs helped engineers at the Wikimedia Foundation design, implement, and deploy a differentially private solution to publish Wikipedia usage metrics in a provably secure way.

Research

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.

Research

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.

Blog Post

Publishing Wikipedia usage data with strong privacy guarantees

Tumult Labs helped engineers at the Wikimedia Foundation design, implement, and deploy a differentially private solution to publish Wikipedia usage metrics in a provably secure way.

Unleash the power and value of your data.

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