Blog

Sometimes we take a break from building cutting edge AI redaction models to stretch our academic muscles and write about privacy and machine learning. Check back here regularly for our musings.

Accelerating Tensorflow Lite with XNNPACK

Accelerating Tensorflow Lite with XNNPACK

The new Tensorflow Lite XNNPACK delegate enables best in-class performance on x86 and ARM CPUs — over 10x faster than the default Tensorflow Lite backend in some cases.

Part 1: Homomorphic Encryption for Beginners

Part 1: Homomorphic Encryption for Beginners

We cover the basics of homomorphic encryption, followed by a brief overview of open source HE libraries and a tutorial on how to use one of those libraries (namely, PALISADE).

Why is Privacy-Preserving NLP Important?

Why is Privacy-Preserving NLP Important?

A number of people ask us why we should bother creating NLP tools that preserve privacy. Apparently not everyone spends hours thinking about data breaches and privacy infringements.

A Brief Overview of Privacy-Preserving Technology Methods

A Brief Overview of Privacy-Preserving Technology Methods

A very brief overview of privacy-preserving technologies follows for anyone who’s interested in starting out in this area. I cover symmetric encryption, asymmetric encryption, homomorphic encryption, differential privacy, and secure multi-party computation.