When you tokenize data, you’re using a sort of fine-grained data protection. A clear value is replaced with a fictitious, randomly generated value that represents the real one. To reduce downstream application changes and improve data sharing, the pattern for tokenized value can be customized and can remain the same format as the original.
Implementing Tokenization definition
To comprehend tokenization, you must believe that in the current data world, outsiders will continue to find increasingly sophisticated and new ways to obtain access to and control over your data and that governments will continue to hold data owners liable for the harm data loss causes the public. After accepting that moats and walls are strong deterrents, the next step is to realize that they lose their effectiveness as an enterprise’s boundaries grow increasingly fractured due to adopting cloud-based services, SaaS, and 3rd party data processing. There are a few ways to do this, one of which is to use fine-grained data protection to safeguard the data itself by transforming a cleartext value like “Bob” into “XYZ,” then using that value for everything from storing to sending to processing.
You might be wondering how we got from ‘Bob’ to ‘XYZ.’ In terms of fine-grained security, tokenization and Format Preserving Encryption are both deemed secure enough for enterprise use (FPE). If you’re looking for a fast, scalable option for operational tokenization, look no farther than FPE or some forms of faultless tokenization. These methods preserve the ability to unprotect all data back to the cleartext value, so even lawful archives can be kept safe. Individual token lifecycle management or analytics data preservation requires a vaulted tokenization solution. Let’s stand back for a second and fill in the blanks. The privacy industry has had difficulty adopting uniform definitions across a wide range of granular security measures. Tokenization has been used to safeguard data for a LONG time, but there are numerous variations on the theme today. If you want to describe how one value replaces another in a data set, you can use the phrase “replacement” without specifying how it was obtained. However, as we’ll see later, the means are critical since they determine many aspects of tokenized data’s eventual usability and security.
With a vaulted tokenization definition strategy, the cleartext values are stored alongside their random tokens in the vault. With this form, you may maintain individual permits and cleartext values over time, making tokenizing data more secure (token lifecycle management). Greater security is also enabled by allowing the domain of viable tokens to go well beyond that of the available cleartext value domains. This means that there are only approximately 1000 distinct ways to tokenize a three-digit number, which means that anyone with access to all tokens might infer the total number of tickets, which could compromise the privacy and security of the underlying data. Adding any number of extra digits to vaulted tokenization prevents this, so the original thousand token values are randomly distributed among around a million possible token values when another three digits are added. These principles apply to all numeric values as well as alpha-numeric ones.
Random values are produced and looked up in the vault to make sure they haven’t previously been mapped to cleartext values for vaulted tokenization. Unprotection is similar in that the lookup is finished when it’s time to unprotect, and the cleartext value is returned. Credit card data has been protected using this way for years, and it is now being used to safeguard other sorts of sensitive data. When operational use-cases have significant throughput needs, and the number of possible token values is nearing its upper limit, vaulted solutions have some limitations. The random value generator may have to make several attempts before it comes up with a new token, which increases the overall time required to perform the activity. Also, as the size of token vaults increases, the response time may decrease, although this is less of an issue with contemporary cloud datastores.