What Masking Techniques Should Be Used In Data Analysis
A set of methods that try to protect a direct identifier is called a mask, which is also classified as a common and vulnerable approach.
Variable suspension involves removing a direct identifier from a data base. Repression is applied in databases that require information for public health research. In these cases, it is unnecessary to identify variables in a particular database.
Shuffling is a method that extracts one value from a file and replaces another value from another layer. This creates the situation by having real value in the data collection, but they are assigned to different people.
Creating artefacts can have two options. Both methods should use individual patient values, such as medical reports or SSN. The first approach involves using one way washes to value using a secret key that must be protected. A hash function creates and converts many different values, except for its original value. The advantage of this method is that it is possible to apply and reproduce later for different data collection. The second method uses a random pseudonym that is locked; It can not be reproduced in the future. Each of these two methods has different uses in different cases.
Randomization limits the identity of the data set, but the values are divided by moisture or random. Once executed correctly, the possibility of reversing masked values could be very minimal. Common randomization cases would create databases for testing software as the data is extracted from the production of databases, as it is masked and sent to the development team for testing. Data is expected to be monitored through a fixed data communication format, the fields are held and have a realistic lookup value.
There are certain companies that utilize technology in tools that do not have significant protection, such as:
- Sound enhancement that is appropriate for continuous parameters. This type is difficult due to too many methods that are developed to remove noise from data. An anti-filter can reduce noise from data and restore baseline. For this purpose, there are many different filter types that are developed when it comes to signal processing of a domain.
- Scorpio features use masking tools that restructure Easter's orders in the field, as NURSE is scrambling to RSUNE. This is easy to reverse with the original.
Abbreviation is a digital variant where the last characters are removed and then replaced by "*". This could have the same risks and symptoms of a mask. Removing the last characters in the last name may, however, lead to 67% more or less of the individual names of the remaining characters.
Encoding means changing values with another meaning meaningless and this requires care for the process because it is easy to do a frequency analysis and this shows how often the names appear. In a multinational database, the most likely frequency is to be SMITH. Encoding should then be solved to create pseudonyms on individual values instead of general masking.
Unsafe masks should not be used, even in practice. If this is done, the depositary is safe.
It is important to note that protective masking technology will significantly reduce the usefulness of the data. So masking should be applied to non-data-sensitive areas, these are often direct identifiers usually associated with names and email addresses that are not part of any data analysis.
Masking technology should also not apply to dates or geographic information because these are the data that are often used in analysis. Masking would make it difficult for successful analysis to take place.
Data authentication data is also based on different features of field styles and changes. Different algorithms are applied when it comes to birthdays versus zip code. There are many sets that consist of semi-identifiable identifiers and direct identifiers. It is best to apply both data protection methods such as de-authentication, masks, and data sensitivity management.
Source by Raheem Olalekan