This year for the first time in its 230-year history the US 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…

Following a personal pitch to Donald Trump from CEO Ginni Rometty, IBM employees are making their distress public.