The Innovation of Compute-to-Data
Buy and sell private data while preserving privacy
The consensus around the removal of personal data from silos, controlled by data monopolies, has never been as compelling as in recent years. It is no longer a question of whether it should be done, but a question of how. Data has proven its value as a commodity, however in centralised markets, between corporations who act as data hoarders and manipulators. This has caused public outrage as these corporations often undermine users’ privacy. Though it is an efficient market, it is not sustainable and raises myriad ethical questions. There are, however, many other well-meaning companies who are sitting on data that has potential to improve their balance sheet, but simply do not know how to monetise it in a secure and private manner.
The key ideas here are Value and Privacy. How do we gauge the value of a data set in the public market in order to facilitate the sale and consumption thereof, in a secure and privacy-preserving manner?
Decentralised Data Marketplaces
Decentralised data markets allow anyone to monetise their data as long as other market participants can agree on the value / price of that data. And as a result, opening up data sets that were previously unavailable for research and artificial intelligence. But, this has not been in the most secure and privacy-preserving manner, until the introduction of Compute-to-Data in Ocean Market.
Ocean Market is a Web3 data marketplace where data publishers allow data consumers (data scientists, researchers, etc.) to train algorithms on their data, while preserving privacy and the data never having to be copied or leave the publisher’s premises. Users can publish both data sets and algorithms — collectively referred to as data assets. The price is either set by the publisher or is left to the devices of the open market through liquidity pools and an automated market maker (AMM).
Compute-to-Data (C2D)
C2D allows for the sharing of data without the data having to leave the premises or compromise the data subjects’ privacy. Consumers purchase compute jobs on the data to improve the accuracy of their A.I models, or to derive relevant insights. The publishers earn from download and compute sales on said data.
Data access types
There are 2 consumption access permissions the publisher can choose from; download and compute:
- Download — this access type would probably be best for non-personal data (e.g climate change related data, etc.)
- Compute — compute access is best for personal data (e.g health records, etc.) whose exposure would likely pose a risk.
Watch the Compute-to-Data Demo on YouTube!
Author: Bhalisa, Ocean South Africa — Project Lead
