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.
Differential privacy is a hot topic given the many conflicting opinions on its effectiveness. For some background, we previously wrote a comprehensive post on the
Over the years, large pre-trained language models like BERT and Roberta have led to significant improvements in natural language understanding (NLU) tasks. However, these pre-trained
At Private AI, we are building a privacy suite centered around personally identifiable information (PII) detection and remediation in unstructured data, such as text. Users interact with our
In the previous episode of Private AI’s ML Speaker Series, Patricia Thaine (CEO of Private AI) sat down with Dr. Aida Nematzadeh (Staff Research Scientist
In the previous episode of Private AI’s ML Speaker Series, Patricia Thaine (CEO of Private AI) sat down with Dr. Sarah Shoker (Research Scientist at
In today’s world, large models with billions of parameters trained on terabytes of datasets have become the norm as language models are the foundations of
There are several resources available on the internet on how to scale your Kubernetes pods based on CPU, but when it comes to Kubernetes pods
In the previous episode of Private AI’s ML Speaker Series, Patricia Thaine (CEO of Private AI) sat down with Arvid Frydenlund (PhD candidate at the University
Personally Identifiable Information (PII) is any data that can be used to identify an individual. This can be done using direct identifiers (name, social security
Previously on Private AI’s speaker series CEO, Patricia Thaine, sat down with Franziska Boenisch to discuss her latest paper, ‘When the Curious Abandon Honesty: Federated Learning Is Not Private’. Franziska
In the latest episode of Private AI’s ML Speaker Series, Patricia Thaine (CEO of Private AI) sits down to chat about MLOps and Machine Learning Deployment
With the ever-growing number of global regulations, legislations, and amendments, it can be overwhelming to know where to start (or continue) your data privacy journey.
Transformer networks have taken the NLP world by storm, but the sheer size of these networks presents new challenges for deployment, such as how to provide acceptable latency and unit economics.
Previously on Private AI’s Speaker Series, our CEO Patricia Thaine sat down with data privacy law expert Carol Piovesan to talk about the legal ramifications
Data privacy, in simplest terms, is the right to control how your personal information is collected and used. Although this may seem obvious, it hasn’t
Carole Piovesan discusses legal responsibilities, what companies are getting wrong with data governance, and more.
GDPR compliance, privacy and engineering team collaboration, and common mistakes companies make with their data.
Discussing developer responsibility, Bill C-11, positive consent, and the importance of Privacy by Design
“When is anonymization useful?” is a tricky question, because the answer is highly data-type- and task-dependent.
On the misleading ways journalists and industry use the term "anonymization."
There’s a saying ‘the last 20% of the work takes 80% of the time’ and nowhere is that more true than AI systems.
Regexes are highly effective in the perfect world of computer data, but unfortunately the real world is much more complicated.
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?
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.
Privacy Enhancing Technologies Decision Tree: for developers, managers, and founders looking to integrate privacy into their software pipelines and products.
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.
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.
Some techniques to improve DALI resource usage & create a completely CPU-based pipeline.
We introduce the four pillars required to achieve perfectly privacy-preserving AI and discuss various technologies that can help address each of the pillars.
We discuss a practical application of homomorphic encryption to privacy-preserving signal processing, particularly focusing on the Fourier transform.
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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).
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 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.