Friday IBM Research updated their open source “IBM Differential Privacy Library,” a suite of new lightweight tools offering “an array of functionality to extract insight and knowledge from data with robust privacy guarantees.”
“Most tasks can be run with only a single line of code,” brags a new blog post (shared by Slashdot reader IBMResearch), explaining how it works:
This year for the first time in its 230-year history the U.S. Census will use differential privacy to keep the responses of its citizens confidential when the data is made available. But how does it work? Differential privacy uses mathematical noise to preserve individuals’ privacy and confidentiality while allowing population statistics to be observed.
This concept has a natural extension to machine learning, where we can protect models against privacy attacks, while maintaining overall accuracy. For example, if you want to know my age (32) I can pick a random number out of a hat, say ±7 — you will only learn that I could be between 25 and 39. I’ve added a little bit of noise to the data to protect my age and the US Census will do something similar.
While the US government built its own differential privacy tool, IBM has been working on its own open source version and today we are publishing our latest release v0.3. The IBM Differential Privacy Library boasts a suite of tools for machine learning and data analytics tasks, all with built-in privacy guarantees. Our library is unique to others in giving scientists and developers access to lightweight, user-friendly tools for data analytics and machine learning in a familiar environment… What also sets our library apart is our machine learning functionality enables organisations to publish and share their data with rigorous guarantees on user privacy like never before…
Also included is a collection of fundamental tools for data exploration and analytics. All the details for getting started with the library can be found at IBM’s Github repository.
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Source:: Slashdot