Privacy-Intact Remote Data Analysis
Learn from data without the liability of handling it
Blind Query provides customers the ability to analyze data and learn about and build summary reports on remote, protected datasets without ever needing to obtain or handle a copy of the raw data. Users can search, join, and run analyses including summary statistics on data that remains in place across multiple geographical or organizational silos. Because the data never moves, approved queries and reports are always run where the data resides, on demand, even when the request is made by an outside party.
What is Blind Query?
A set of functions for analyzing protected data
When datasets cannot be physically pulled together for analysis, due to regulatory or organizational barriers, data practitioners still need ways to learn from the data and to extract insights from it. Blind Query provides the solution.
How Blind Query Works
Blind Query works through a suite of tools for performing
Blind String Searches
Join two or more datasets without seeing or revealing too much
Blind Join makes it possible to operate across multiple tabular datasets using SQL-like methods to identify rows with equal or similar (fuzzy) values and then extract other values on the same row to merge with your dataset, all without revealing anything about the non-matched data in any of the tables. Data providers control and permission exactly which columns can be extracted from their datasets for matching values.
What Makes Blind Join So Unique?
Works at a high volume (millions of records).
Big data requires software solutions which scale with the size of the data operations. Blind Join works on datasets big and small, so businesses do not miss a step when they scale up their processing.
Only ever returns allowed fields.
Welcome to true, granular data asset control. Our users designate exactly which columns from their proprietary datasets can be returned during approved join operations. Requests for Blind Join operations which violate the acceptable terms are blocked.
Enables private fuzzy (non-exact) matches.
Blind Join extends beyond exact matching. Users can calibrate the tolerance level to return partial or close matches. Data providers set rules for how much “fuzziness” is allowed, and the technology ensures those rules are followed every time.
Blind String Search
Search text in tabular data for terms without seeing the actual data
Freely explore text data without exposing the raw data. Blind String Search allows users to find matches and generate summary counts without ever needing to look at raw data. Data providers are maximally protected, while data users can extract precisely the necessary and actionable information they need from the datasets – no more, no less.
Understand populations scattered across multiple remote datasets.
Blind Stats provides businesses with the ability to run statistical analyses on data that is distributed geographically or across different business units.
Don’t move the data. Just gather the results and get to actionable insights faster.
Real-world data (RWD) and real-world evidence (RWE) are playing an increasing role in health care decisions.
– FDA on Real-World Evidence
Launch into Action
Understanding your study population is the foundational first step involved in all analytics. With Blind Stats, data practitioners can move through that step faster, cheaper, and easier than ever before, supercharging timelines and delivering value.
Less time figuring out how to collaborate
More time validating, investigating, and solving important problems.
Book A Demo
TripleBlind keeps both data and algorithms in use private and fully computable. To learn more about Blind Learning, or to see it in action, please book a demo!