Defining Machine Learning Security
Organizations trust machine learning (ML) to perform a wide variety of tasks today because it has proven to be relatively fast, inexpensive, and effective. Unfortunately, many people really aren’t sure what ML is because television, movies, and other media tend to provide an unrealistic view of the technology. In addition, some users engage in wishful thinking or feel the technology should be able to do more. Making matters worse, even the companies who should know what ML is about hype its abilities and make the processes used to perform ML tasks opaque. Before making ML secure, it’s important to understand what ML is all about. Otherwise, the process is akin to installing home security without actually knowing what the inside of the home contains or even what the exterior of the home looks like.
Adding security to an ML application involves understanding the data analyzed by the underlying algorithm and considering the goals of the application in interacting with that data. It also means looking at security as something other than restricting access to the data and the application (although, restricting access is a part of the picture).
The remainder of this chapter talks about the requirements for working with the coding examples. It’s helpful to have the right setup on your machine so that you can be sure that the examples will run as written.
Get in touch
Obviously, I want you to be able to work with the examples, so if you run into coding issues, please be sure to contact me at John@JohnMuellerBooks.com.
Using the downloadable source code will also save you time and effort. With these issues in mind, this chapter discusses these topics:
- Obtaining an overview of ML
- Defining a need for security and choosing a type
- Making the most of this book
