You’ve heard ALTR CTO James Beecham compare encryption to duct tape before. Duct tape is great - we all love duct tape. It comes in handy when you need a quick fix for a thousand different things or even...to seal a duct. But when it comes to security, you need tools that are fit for purpose. Today, let’s take out our tape measure to compare tokenization vs encryption and even anonymization.
As a data security company, ALTR uses encryption for some things, but we found tokenization far superior for defeating data thieves and enabling data sharing. Companies who want to transform data into business value need both security and collaboration. Tokenization delivers the best of both worlds: the strong at-rest protection of encryption and the analysis opportunity provided by anonymization.
Tokenization vs Encryption: 3 Reasons to Choose Tokenization
1.Tokenization replaces the original data with a token.
If someone successfully obtains the digital token, they have nothing of value. There’s no key and no relationship to the original data. The actual data remains secure in a separate token vault.
This is important because we now collect all kinds of information as a society. People want to analyze their data, whether it’s Netflix, a hospital or a bank. If you’re using encryption to protect the data, you must first decrypt it all to make any use of it or any sense of it.
2. Tokenization offers determinism, which allows people to perform accurate analytics on the data in the cloud.
If you provide a particular set of inputs, you get the same outputs every time. Deterministic tokens represent a piece of data in an obfuscated way and give you back the same token or representation when you need it. The token can be a mashup of numbers, letters and symbols, just like an encrypted piece of data, but tokens preserve relationships. The real benefit of deterministic tokenization is allowing analysts to connect two datasets or databases securely, protecting consumer privacy while allowing analysts to run their data operations.
3. Tokenization allows you to retrieve the original data in the event you need it.
Let’s say you’ve collected instrument readings from a personal medical device that I own. If you detect something in that data, like performance degradation, you and I both would appreciate my getting a phone call, an email or a letter informing me I need to replace the device. Encryption would not allow this because once data is encrypted, such as my name or phone number, it disappears forever from the database.
Tokenization vs Anonymization: Anonymization limits analysis today and tomorrow
In contrast, anonymization offers the ability to perform some fundamental analysis, but you’re limited by the original data design and intent. Anonymization removes all the PII by grouping data into ranges, like sorting a list of customers by age or zip code while removing their birthdate and social security number. This means you can perform a level of analysis on anonymized data, say on your 18 to 25 years old customers. But what if you wanted a different group or associate that age range with another data set?
Like encryption, anonymization is permanent and inflexible. The process cannot be reversed to re-identify individuals, which might not give you enough options. If your team wants to follow an initial data run to invite a group of customers to an event or send them an offer, you’re stuck without the phone number or mailing address available. There’s no relationship to the original PII of the individual.
Anonymization and encryption, therefore, can be shortsighted moves. Your organization’s success depends on allowing authorized users to access the original data now and in the future, as long as you can track and report on the usage. At the same time, you must also ensure that sensitive data is useless to everyone else.
Tokenization overcomes these challenges by preserving the connections and relationships between data columns and sets. However, tokenization isn’t just a simple mathematical scramble of the original data like encryption or a group of ranges with anonymized data. Authorized analysts can query tokenized data for insights without having access to the underlying PII. The more secure token remains meaningless to any unauthorized user or hacker.
With modern tokenization techniques, you can apply policies and authorize access at scale for thousands of users. You can also track and report on the secure access of sensitive data to ensure compliance with privacy regulations worldwide. You can’t do this with anonymization and encryption.
When it comes to tokenization vs encryption or anonymization, tokenization is the more flexible tool for secure access and privacy compliance. This is critical for organizations quickly moving from storing gigabytes to petabytes of data in the cloud. You can feed tokenized data directly from cloud data warehouses like Snowflake into any application. You can do this with complete confidence that all the data, including sensitive PII, will be protected even from the database admin while making it easy for authorized data end-users to collaborate and deliver valuable insight quickly. Isn’t that the whole point?