Both methods should employ distinctive patient values like medical record numbers or SSNs. The first approach involves applying an one way hash to a value with the use of a secret key which in turn, must be protected. A hash function creates and converts numerous different values, except for its original value. The benefit of this method remains that it may be applied and recreated later for a different data set. Each one of the two approaches has different uses for different cases. Randomization limits the identifiers in the data set, but the values are replaced with rake or arbitrary values.
Once executed properly, the ability to invert the masked values will be very minimal. Common cases for randomization will be creating data sets for testing software where the data is extracted from production databases, where it’s masked after, and sent to team of developers for testing. Data is likely to follow a fixed data scheme format, the fields are maintained and have realistic looking values. This type is problematic because of too many techniques which are being developed to remove noise from the data. An opponent using filters can extract the noise from the data and retrieve the original values.
Character Scrambling make use of masking tools that reorganize character orders in the field as NURSE being scrambled to RSUNE. This is simple to reverse to its original. Truncation is a character masking variation where the last few characters are removed and then replaced with. This could present the same risks as character masking. Encoding means replacing a value with another value that’s meaningless, and this requires care for the process because it’s simple to do a frequency analysis and this shows how usually the names appear. In a multiracial data set, the most frequent they are more than likely to be SMITH.