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What is Data Abstraction?

Data abstraction is the creation of a simplified representation of the information in a database: by representing its essential, aggregated features while hiding unnecessary user-level details, data abstraction protects the privacy and confidentiality of data while preserving a machine’s ability to learn from it. Abstractions are anonymous, aggregated, irreversible representations of data sets. The Fantix app generates them locally in your system or cloud instance so that no user level information ever leaves your servers. Your data is safe because we don’t transmit or transfer it anywhere.

Is Fantix a Data Cleanroom?

Fantix is not a data cleanroom. We aim to replace data clean rooms in use cases where probabilistic, synthetic data is nearly as good (or better) than real-world data: by removing the risk of transmitting real user-level data, Fantix can make clean rooms un-necessary in many use cases.

Is Fantix a Mobile App?

No. Fantix is a cloud application and a desktop application that runs on MacOS, Windows and Linux. We do not currently have any apps for iOS and Android.

How does Fantix protect Personally Identifiable Information (PII)?

Fantix does not collect Personally Identifiable Information.

Why is abstraction different from and better than aggregation?

Data aggregation does not offer full protection: there are techniques to reverse the process and extract personal information from aggregated data. An abstraction is not reversible because it is not data: it is a representation. What is so innovative about Fantix is that our application does not process data, it understands it. It understands data and collects it as an abstraction. A single abstraction is meaningless, but it has the power to produce insights when compared with other abstractions.

What is synthetic data?

Synthetic data is artificially generated information that mimics real-world data characteristics. It serves as a privacy-conscious, scalable, and customizable alternative for UX personalization, targeting, and product development without using actual, potentially sensitive, data.