This talk will start with an overview of current methods of privacy preserving computation including homomorphic encryption, secret sharing, and differential privacy.
We will explore current uses of privacy preserving computation, in fields such as government, finance, telecommunications and IoT. We will look at the technological and social barriers to wider adoption then gaze into the crystal ball to see where the future might take us.
To conclude we will explore one technique (partially homomorphic encryption) with a motivating medical example. A high level overview of the open source library python-paillier will be given along with a few code samples showing how easy (some) privacy preserving analytics can be to implement.