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.
Data Protection Regulations NLP Teams should Watch for in 2022
Natural Language Processing (NLP) teams need to stay up to date with the latest data protection regulations when training their models with user data. Not only do customers continue to demand privacy, but according to the GDPR they must give positive consent for their...
Data Privacy, Law, and Cybersecurity with Carole Piovesan
Carole Piovesan discusses legal responsibilities, what companies are getting wrong with data governance, and more.
An interview with Sheila Jambekar, CPO at Plaid
GDPR compliance, privacy and engineering team collaboration, and common mistakes companies make with their data.
Talking with Dr. Ann Cavoukian, Privacy by Design inventor
Discussing developer responsibility, Bill C-11, positive consent, and the importance of Privacy by Design
Anonymized Data is Useless: Fact or Fiction
“When is anonymization useful?” is a tricky question, because the answer is highly data-type- and task-dependent.
Data Anonymization: Perspectives from a Former Skeptic
On the misleading ways journalists and industry use the term “anonymization.”
Demystifying De-identification
Understanding key tech for data protection regulation compliance
What It Really Takes to Build An AI System: It’s more complicated than many think
There’s a saying ‘the last 20% of the work takes 80% of the time’ and nowhere is that more true than AI systems.
Natural Language v. Regex: The Context wars
Regexes are highly effective in the perfect world of computer data, but unfortunately the real world is much more complicated.
Privacy Tech: Understanding Internet Security
There exists a vibrant ecosystem of specialized security tools. The sad truth is that it is almost impossible to reach 100% invulnerability. What can we do to get closer?
Customers Are Demanding Privacy
In the past three years there has been a massive wake-up in customer awareness about privacy. Many customers are now refactoring how they buy, taking their business elsewhere if they don’t trust a company’s data practices.
Liability & AI Malfunction: An AI System Developer’s Perspective
AI is rapidly being deployed around the world with few to follow. Along with the complexity of creating the technology, there remain many unanswered legal questions.
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.
NVIDIA DALI: Speeding up PyTorch
Some techniques to improve DALI resource usage & create a completely CPU-based pipeline.
The Four Pillars of Privacy-Preserving AI
We introduce the four pillars required to achieve perfectly privacy-preserving AI and discuss various technologies that can help address each of the pillars.
Part 2: Homomorphic Encryption for Beginners
We discuss a practical application of homomorphic encryption to privacy-preserving signal processing, particularly focusing on the Fourier transform.
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?
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 Software 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.