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| Monetise your data while preserving privacy |
Compute to data
Introduction
There are many datasets that are too sensitive to be sold, such as health records or other personal information. Compute-to-Data enables you to make money from these datasets while keeping the data private. Rather than selling the raw data, you can sell compute access to the private data.
You decide which algorithms you allow to be run on your dataset. So, for example, if you own sensitive health records you could allow an algorithm that outputs the average age of a patient but no other information.
Compute-to-data resolves the tradeoff between the benefits of using private data, and the risks of exposing it. It lets the data stay on-premise, yet allows 3rd parties to run specific compute jobs on it to get useful compute results like averaging or building an AI model.
The most valuable data is private data — using it can improve research and business outcomes. But concerns over privacy and control make it hard to access. With Compute-to-Data, private data isn’t directly shared but rather specific access to it is granted.
It can be used for data sharing in science or technology contexts, or in marketplaces for selling private data while preserving privacy, as an opportunity for companies to monetize their data assets.
Private data can help research, leading to life-altering innovations in science and technology. For example, more data improves the predictive accuracy of modern Artificial Intelligence (AI) models. Private data is often considered the most valuable data because it’s so hard to get at, and using it can lead to potentially big payoffs.
We suggest reading these guides to get an understanding on how compute-to-data works:
User Guides
- How to write compute to data algorithms
- How to publish a compute to data algorithm
- How to publish a dataset for compute to data