New Compute-to-Data

Buy & Sell Private Data, While Preserving Privacy

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.

  • Control

    Data owners retain control of their data, since the data never leaves the premises.

  • Huge datasets

    Data owners can share or sell data without having to move the data, which is ideal for very large datasets that are slow or expensive to move.

  • Compliance

    Having only one copy of the data and not moving it makes it easier to comply with data protection regulations like GDPR.

  • Auditability

    Compute-to-data gives proof that algorithms were properly executed, so that AI practitioners can be confident in the results.

How It Works

Data owners approve AI algorithms to run on their data. Compute-to-Data orchestrates remote computation and execution on data to train AI models, while preserving the privacy of the data.

Private data aids in research, leading to life-altering innovations in business, science and technology. For example, more data improves the predictive accuracy of modern AI models.

Marketplaces can allow their users to publish data sets with Compute-to-Data enabled, in addition to access via file download.

In addition to Ocean Protocol core components, a Compute-to-Data infrastructure is set up as a Kubernetes (K8s) cluster e.g on AWS or Azure in the background. This Kubernetes cluster is responsible for running the actual compute jobs, out of sight for marketplace clients and end users.

Marketplaces choose what exact compute resources they want to make available to their end users within this K8s cluster, even have them choose from a selection of different images and resources.

Likewise, marketplaces can choose and restrict the kind of algorithm they want to allow their users to run on the data sets in a marketplace.

Compute-to-Data Resources