In the field of data science, one of the most common questions asked is, What’s the difference between cryptography and machine learning? The truth is that they are both similar in some ways, but they are also very different in many others. In this article, we will take a deep dive into the differences between cryptography and machine learning, so you can better understand how they apply to your project at hand.
Definition
Cryptography and machine learning are two of today’s most popular fields, with both bringing in a great deal of money for programmers and businesses alike. However, there are often debates on what separates cryptography from machine learning. While they may seem similar on surface level, they’re actually very different branches that require different skill sets.
If you don’t know what separates them or how you can use both, here’s your guide to understanding cryptography vs machine learning in depth.
What is Cryptography
Cryptography (from Greek κρυπτός, kryptós, hidden, secret) is an art and science of keeping information secret. More specifically, cryptography is used to protect information.
If you encrypt your data or communications in such a way that no one without a key can read it, then only people with a key can access it. You must trust in those who hold these keys not to abuse their power over your privacy. The cryptographic technologies that exist today provide varying degrees of security against different threats. There are two main types of cryptography, symmetric and asymmetric cryptography.
What is Machine Learning
Machine learning (ML) is a subfield of computer science that builds systems that can learn. These systems require examples, or data sets, to draw inferences from.
There are three kinds of machine learning: supervised, unsupervised and reinforcement learning. Within those broad types are many subtypes. In supervised learning, also called predictive modeling, algorithms analyze sets of known inputs and outputs to find patterns in them; they then use these patterns to predict future behavior based on new inputs. In unsupervised learning, algorithms discover hidden insights by sorting through large data sets without prior information about their contents or labels.
Where Machine Learning Meets Cryptography
Many people are under the impression that cryptography and machine learning are two entirely different things.
In reality, cryptography requires some form of input, whether it be a private key or user information that can provide an adequate source of machine learning. The two are so close in practice, they require one another to create a beneficial algorithm. Without any data to analyze and patterns to discover, you would never know what algorithms need to be created in order for them to work.
This means you must have a basic understanding of cryptography before attempting any kind of machine learning program. Understanding what each does individually isn’t too difficult, but understanding where one begins and one ends takes time.
You may check this article of Dr. Robert Kübler to know more about it.
Example of Cryptography in Applications
Cryptographic hash functions are an important tool in secure communication. They allow two parties to scramble information, so that it becomes unrecognizable and difficult to decrypt by a third party.
However, there are numerous cryptographic hash functions (and protocols) out there—and they’re not all equally strong. The most popular of these include MD5, SHA-1, SHA-2, and SHA-3 (aka Blake2b).
While these functions will continue to get better over time as computer power increases, researchers keep working on new methods for encryption that go beyond simple cryptographic hashing.
This has led some people to wonder if we will eventually see a cryptographic singularity, where strong encryption can no longer be decrypted using traditional methods. Is something like that even possible?
In short, yes—but before you start panicking about your encrypted data becoming vulnerable to hackers, it’s worth noting that such a thing would require a huge leap forward in computing technology. It’s also worth pointing out that such research is currently happening outside of academia and industry; many experts believe such advancements won’t come from cryptographers, but from machine learning experts instead.
In fact, many argue that cryptography is already dead thanks to advances in machine learning. It’s easy to understand why when you consider how quickly computers have gotten better at breaking crypto lately. Google Brain’s AlphaGo Zero AI learned to play Go with only its own neural network and was able to beat a human champion in just three days.
Meanwhile, Google’s DeepMind AlphaZero learned chess in just four hours, then went on to beat one of the world’s best chess programs after 24 hours. And those were non-chess playing AIs! Clearly, AI has advanced far enough that machines could potentially learn how to break cryptography without any help from humans. So what does that mean for cryptography moving forward?
Well, firstly, it means that more companies will likely begin investing more heavily into machine learning solutions rather than relying solely on cryptography. At least until a true cryptographic singularity happens.
But secondly, it means that cryptography might actually become safer in some ways, too—at least until quantum computing makes all current cryptography obsolete. That said, don’t expect quantum cryptography to save us anytime soon. Quantum computers are still very much theoretical at present and aren’t expected to become practical for another 20 years or so. Until then, we’ll probably have plenty of time to develop new forms of cryptography based on machine learning algorithms.
Example of Machine Learning in Applications
Here are a few examples of machine learning in action, just to give you an idea of how it works. Of course, these aren’t just thrown together; there are entire teams working behind them.
Amazon and Netflix use machine learning on huge sets of data to make predictions and decisions about customer behavior. Google scans over 1 billion search queries every day, which it then uses to refine its knowledge of your interests based on your search history.
Facebook analyzes what kind of content gets shared across its network in order to tell if a user will likely share something new based on their previous history. This is all done automatically by algorithms that are constantly refining themselves as they learn more information. That’s why we call it machine learning. It learns from experience so that humans don’t have to do any work! The only problem with machine learning is that while computers can do certain things faster than humans, we still have to teach them how to do those things first.
So even though machines might be able to recognize faces better than us or process information faster than us, we still need people for other parts of decision-making processes like deciding what kinds of products should be offered or determining who should get a loan or not.
A good example of where machine learning really comes into play is in personalization. If you shop at a store online, chances are they’re using some sort of algorithm to tailor their offerings to your tastes. But it doesn’t stop there; many ecommerce sites now offer different product suggestions based on what you look at online and when you look at them (and sometimes even when you buy something). In fact, some companies say that 80% of their revenue comes from 20% of their customers—so if they can figure out how to sell more stuff to those 20%, they could potentially double their profits without having to sell anything else! How do they know what products each person likes?
They analyze users’ browsing histories and purchase histories to find patterns between individual users. Then, they try to predict what someone would want next based on their past preferences. There are lots of ways to do this, but one common method is called collaborative filtering.
Collaborative filtering involves looking at similar items purchased by other people who also bought that item and seeing if there was a higher chance of purchase after viewing another item in a set.
For example, let’s say I’m shopping for shoes online. I’m currently looking at a pair of brown shoes. After I click add to cart, a pop-up appears saying, people who looked at these shoes also looked at blue shoes. Now, I’m probably going to go look at blue shoes because my goal is to buy a pair of brown shoes. At least, that’s what a computer program would think. And it turns out they’re right. If they show me blue shoes after I add brown ones to my cart, I’ll probably end up buying both pairs. It’s very effective because their goal isn’t just to sell me one pair of shoes; their goal is to increase sales overall.
So if they can sell more by showing me a second pair of shoes, they’ll do it. And that’s just one way to implement machine learning in applications. There are tons of others, and we’ve barely scratched the surface here. But hopefully you get a sense of how it works and what you might expect to see in your daily life if you haven’t already.
Conclusion
In summary, both cryptography and machine learning are important to keep up with. There is a lot of overlap, but these two techniques will continue to evolve as time goes on. Let’s learn more about them, so we can best use them in our field. What do you think of cryptography and machine learning? Are they similar or different? Do you prefer one over another? Please share your thoughts in the comments below! We’d love to hear from you.