Handling PII in Data Projects

Personally Identifiable Information, or PII for short, is an essential aspect of data science work today. It involves sensitive data that can compromise the identity of at least some of the people involved in a dataset (e.g., someone's name, financial data, address, phone number, etc.). PII is particularly important today as it's protected by law in many countries, and any violation of this sort of data can fetch huge fines. What's more, PII is often essential in data science projects as it carries useful information that can bring about a sense of personalization to the data products developed.

Due to various factors, such as using multiple data streams, datasets used in data science today are full of PII. Note that PII can result from a combination of variables since there isn't an infinite amount of people. As a result, given enough information-rich variables, you can predict several PII variables with reasonable accuracy. This ability to predict PII makes the problem even more severe since PII can be a serious liability if it leaks. As there is plenty of it in modern datasets, the risk of this happening grows with the more data you gather for your data science project.

Of course, you could remove PII from your dataset, but it's not always a good option. After all, much of this PII is useful information that can help with the models built. So, even if you can eliminate certain variables, the bulk of PII will need to be retained for the models at hand to be useful and a value-add to your data science project. As for obscuring the PII variables (e.g., through a method like PCA), this is also a valid option. However, with it, any chance of transparency in your models goes out the window.

Fortunately, you can protect PII with various cybersecurity methods, without compromising your models' performance or transparency. Encryption, for example, is one of the most widely used techniques to keep data secure as it's turned into gibberish when not in use. In some cases, even in that gibberish state, you can perform some operations for additional security. However, in most cases, the protection is there for the time the data is in transit, which is when it's also the most vulnerable.

Since nowadays the use of the cloud for both storing and processing data is commonplace, the risk of exposing PII is more significant than ever. Fortunately, it's not too difficult to have security even in these situations, as long as the cloud provider offers this protection level. It just needs to have that in the platform it uses and in all the network connections involved.

Hostkey is a Dutch company providing cloud services, targeted towards data science professionals. Just like most modern cloud providers, it offers high-quality cybersecurity for all the data handled. At the same time, if you are super serious about this matter, you can also lease a dedicated server from it. Additionally, Hostkey offers GPU servers, which are a bigger bang for your buck when it comes to high-performance data models, such as deep learning ones. So, check out this cloud company and see how you can benefit from its services. Cheers!