Data masking has become increasingly important as more and more sensitive data finds its way into the hands of unauthorized users. In fact, most organizations have certain data that needs to be protected from misuse or theft, but the sheer volume of this data makes it impossible to determine what exactly needs to be encrypted or redacted and which information can be left unprotected without putting the company at risk. Fortunately, several data masking tools can take care of this problem, and in this article, you’ll learn about seven different techniques you can use to secure your sensitive data using these tools.
Which Data needs Data Masking?
The following are some examples of data that require data masking:
(a) Protected health information (PHI)
This is any information related to an individual’s health status, treatment, or payment for health care. PHI includes demographic data such as name, address, birth date, phone number, and email address. It also includes any information about a person’s past or present medical condition or history that relates directly to that person’s physical or mental health.
(b) Personally identifiable information (PII)
This is any information that uniquely identifies an individual, including a person’s name, driver’s license number, place of birth, mother’s maiden name, or biometric records. It also includes other information such as physical data such as fingerprints and retina scans.
(c) Payment card information
This is any information necessary for a thief to create fraudulent copies of credit cards, such as account numbers, CVV numbers, or expiration dates. In addition, data related to debit card PINs are also considered sensitive information that needs special protection.
Types of Data Masking
There are many types of data masking tools including;
– Dynamic data masking
This involves using a simple rule, such as 00 for every third digit in a Social Security number, to mask data. It is often used on columns that are not frequently accessed.
– Static data masking
This involves modifying data in place, so that it can be read, but not changed. For example, masking a Social Security number with Xs or 9s. This type of masking is ideal for columns that are frequently accessed.
– On the fly data masking
This involves modifying data in-flight, as it is being transmitted before it is received by other applications. This type of masking is ideal for data that crosses network boundaries or must be accessed by multiple users.
7 Data Masking Techniques
The following seven data masking techniques are among those most commonly used by organizations today. It’s important to note that, while these methods can be useful for securing sensitive data, they are not intended as a replacement for strong encryption or secure deletion.
1. Data Pseudonymization
This is a method of masking sensitive data that replaces specific fields with unique values. For example, a credit card number could be replaced with a unique code. When a customer provides his or her credit card number, it can be matched up with its pseudonymized value to retrieve its true value. The technique is relatively easy to implement and doesn’t require significant changes to existing systems, which makes it an attractive option for many businesses. It also allows you to use your original dataset for additional analysis without compromising your customers’ information—you just need to replace any sensitive fields before doing so.
2. Data Anonymization
This is similar to pseudonymization, but it does not allow for the recovery of sensitive data. Instead, each record’s fields are replaced with ones that contain randomized data. For example, rather than replacing a credit card number with a unique code, an anonymized record could contain a randomly generated string of numbers and letters. This makes it very difficult for third parties to connect anonymized records back together to create a single customer profile or other collection of sensitive information.
3. Lookup substitution
This technique uses a look-up table to replace specific fields in your data with random data. Each lookup table contains a collection of known sensitive values, such as usernames or email addresses, and their associated replacements. When a sensitive value is found in your dataset, it can be replaced with one from its associated lookup table entry. A different table can be used for each record type—for example, separate tables could be used for usernames, emails, and telephone numbers.
4. Encryption
Encryption is often used in conjunction with other data masking techniques. There are several different encryption algorithms, but most work by encrypting a record’s sensitive data before it is stored or transmitted. When it’s retrieved or accessed, you can use a decryption key—the same one used when it was originally encrypted—to convert it back into its original form. Encryption prevents unauthorized parties from accessing your information without that key.
5. Redaction
Like encryption, redaction is often used in conjunction with other data masking techniques. This is a form of security through obscurity, where only those with specific permissions have access to your sensitive data. In most cases, it’s applied by removing all or parts of records that contain sensitive information, such as usernames or credit card numbers.
6. Averaging
This technique is also known as statistical disclosure limitation. It replaces specific fields with an average value found in your dataset. It’s a great option if you don’t have access to any customer data or you need a universal form of protection that works for all customers in your database, regardless of how sensitive their information is. For example, rather than storing each customer’s actual address, you could store a zip code—then convert it into its latitude and longitude before retrieving it from storage.
7. Date Switching
Last but not least, we have date switching. This technique replaces specific fields with a random value found in your dataset on a given date. For example, you could store all of your customers’ credit card numbers for an entire year before swapping them out for new ones on December 31st. By changing them once per year, you can prevent unauthorized parties from using old credit card numbers to make fraudulent purchases or steal personal information from your database.
Conclusion
In short, data masking is a powerful security tool that can help you protect your sensitive data from being accessed by unauthorized users. It is a process of protecting sensitive information by replacing it with fictitious information so that even if hackers or other malicious entities get access to your database, they will not be able to find any useful information.
