It turns out that operational data exposure swamps out all other kinds of data exposure and data security issues in ML, something that came as a surprise.
Check out this darkreading article detailing this line of thinking.
An important part of our mission at BIML is to spread the word about machine learning security. We’re interested in compelling and informative discussions of the risks of AI that get past the scary sound bite or the sexy attack story. We’re proud to introduce a bi-monthly video series we’re calling BIML in the Barn.
Our first video talk features Maritza Johnson, a professor at UC San Diego and an expert on human-centered security and privacy. As you’re about to see, Maritza combines real-world experience from industry, teaching, and research, making her message relevant to a wide audience.
Here’s Maritza!
The (extremely) local paper in the county where Berryville is situated (rural Virginia) is distributed by mail. They also have a website, but that is an afterthought at best.
Fortunately, the Clarke Monthly is on the cutting edge of technology reporting. Here is an article featuring BIML and Security Engineering for Machine Learning.
Have a read and pass it on!
I gave a talk this week at a meeting hosted by Microsoft and Mitre called the 6th Security Data Science Colloquium. It was an interesting bunch (about 150 people) including the usual suspects: Microsoft, Google, Facebook, a bunch of startups and universities, and of course BIML.
I decided to rant about nomenclature, with a focus on RISKS versus ATTACKS as a central tenet of how to approach ML security. Heck, even the term “Adversarial AI” gets it wrong in all the ways. For the record, we call the field we are in “Machine Learning Security.”
Here is one of the slides in my deck. You can get the whole deck here.
In our view at BIML, every attack has a one or more risks behind it, but every risk in the BIML-78 does not have an associated attack. For us, it is obvious that we should work on controlling risks NOT stopping attacks one at a time.
Another week, another talk in Indiana! This time Purdue’s CERIAS center was the target. Turns out I have given “one talk per decade” at Purdue, starting with a 2001 talk (then 2009). Here is the 2021 edition.
What will I be talking about in 2031??!
BIML founder Gary McGraw delivered the last talk of the semester for the Center for Applied Cybersecurity Research (CACR) speakers series at Indiana University. You can watch the talk on YouTube.
If your organization is interested in having a presentation by BIML, please contact us today.
Some nice coverage in the security press for our work at BIML. Thanks to Rob Lemos!
As our MLsec work makes abundantly clear, data play a huge role in security of an ML system. Our estimation is that somewhere around 60% of all security risk in ML can be directly associated with data. And data are biased in ways that lead to serious social justice problems including racism, sexism, classism, and xenophobia. We’ve read a few ML bias papers (see the BIML Anotated Bibliography for our commentary). Turns out that social justice in ML is a thorny and difficult subject.
We were joined this week by Martiza Johnson, a Computer Scientist and the inaugural director of a new center for data science, AI, and society at the University of San Diego. Maritza assigned us some homework (reading Chapter One and Chapter Four of Data Feminism, this blog entry, and watching Coded Bias), and then led us in a very interesting and far ranging conversation on bias in ML.
We recorded our conversation with Maritza which you can listen to. A video of our conversation is below.
We were very fortunate to have Melanie Mitchell, author of Artificial Intelligence: A Guide for Thinking Humans (and famous programmer of Copycat), join us for our regular BIML meeting.
We discussed Melanie’s new paper Abstraction and Analogy-Making in Artificial Intelligence. We talked about analogy, perception, symbols, emergent computation, machine learning, and DNNs.
A recorded version of our conversation is available, as is a video version.
We hope you enjoy what you see here. This is what BIML meetings are like.
An important part of BIML’s mission as an institute is to spread the word about our understanding of machine learning security risk throughout the world. We recently decided to take on three college and high school interns to provide a bridge to academia and to inculcate young minds early in the intricacies of machine learning security. We introduce them here in a series of blog entries.
We are very pleased to introduce Aishwarya Seth who is a BIML University Scholar.
Aishwarya is a graduate student at North Carolina State University in Raleigh, North Carolina. An ardent fan of crime thrillers since early childhood, she has always been passionate about security. When Aishwarya was introduced to Java programming in high school, her interest in security took a turn towards computer security.
The rise of Machine Learning coincides directly with Aishwarya’s study of security and cryptography, the confluence of which fascinate her. After earning her undergraduate degree in Computer Science, Aishwarya worked as a team member of the Clari5 AI/ML team where she focused on reducing the number of false positives detected for potentially fraudulent transactions online.
Apart from pondering different ways to secure the world, Aishwarya likes to read novels, scribble, travel, and explore.
As BIML University Scholar, Aishwarya will: