Data Providers
Sell the insight. Protect the individual.
A data provider answers to both the people in the data and the customers who buy it. Nakato's twinning process changes every identifying detail before a record leaves a company, so buyers get records that behave realistically without revealing the identities behind them.
Two obligations
Privacy upstream, utility downstream
The people your data describes are owed privacy. The customers who buy it are owed a dataset that is faithful enough to use. It appears as though a choice has to be made between privacy and utility, since every step that ensures privacy seems to reduce usefulness.
A twinned record does both at once. It changes what identifies a person and keeps what the analysis needs. You can protect your data subjects and sell the data at the same time.
A dataset worth buying behaves like the real thing. A twinned one does.
The twin changes who the record is about, without altering what the customer is buying.
What buyers get today
Safe and thin, or detailed and hollow
Most providers share sensitive data in one of two ways. Summary statistics keep identities private but lose row-level detail. Masked records keep the rows but lose the detail in them: the name, date and postcode go, and with them the patterns a buyer wanted to find.
Masking also leaves a quieter risk behind. It catches the obvious identifiers but misses the indirect ones - contextual facts like a job title or rare condition that, put together, point to one person.
Record · Consumer panel
| Field | Originalstays with you | Twinwhat the model sees | Preserved |
|---|---|---|---|
| Panellist | Priya Shah | Anika Rao | gender, name origin |
| Age | 34 | 35 | age band |
| Postcode | LS6 3HJ | LS7 2QT | region, area type |
| Household income | £42,000 | £43,500 | same band |
| Purchases last month | 14 | 14 | unchanged |
Privacy
Nobody in the dataset can be picked out
Nakato reads each record for meaning, so it finds the indirect identifiers as well as names and numbers, and twins those too. What leaves you is a dataset in which every value is plausible and coherent, without pointing to a real person.
One record, twinned: same shape, same context, different face.
Utility
A truer picture of the data
A twin maintains the age band, format, and relationships between fields. Buyers can run record-level analysis and train models on it, rather than working around blanks.
The layer runs within your own environment, on the data you already hold, and works with the architecture you already use. How it works.