Analytics

The following analytic use cases will help you understand how to use TripleBlind to conduct privacy-preserving analytics on 3rd-party data.

Use Case #1: Blind Join (SDK)

Using the TripleBlind SDK, join two or more tabular datasets on the intersection of a given column and return any columns for the intersection subset. Personas represented in this use case are: Data Scientist or Data Analyst (User), Dataset Owner.

Workflow

The following workflow is used to perform this analysis using TripleBlind.

1. Initialize a TripleBlind session
2. Register new assets or locate existing assets
3. Explore assets
4. Run an analysis process and get results

Steps

To execute this use case follow these steps in your Python IDE:

1. The User authenticates with the TripleBlind Router and starts a Session.

import tripleblind as tb
tb.initialize(api_token=user1_token)

ℹ️The call to tb.initialize is unnecessary if the User token is set up in the User’s tripleblind.yaml file.

2. The User registers a dataset as a new asset.

   asset_0 = tb.Asset.position(
       file_handle=data_dir / "store_transactions.csv",
       name=f"Shop Transaction-{run_id}",
       desc="Fictional retail transaction data.",
       is_discoverable=True,
       cost=1,
   )

Or, searches for an existing dataset by name or UUID.

asset0 = tb.TableAsset.find("Shop Transaction")
# or
asset0 = tb.TableAsset.find("673b8bd1-e758-4d56-b6c1-4e1ff946f1c7")