Final answer:
A dataset attribute known as a Quasi-identifier needs to be protected, as it could potentially identify an individual when combined with other data. Using secondary data is considered nonreactive or nonrestrictive. Qualitative or categorical data like hair color or blood type describe attributes of a population and are not suited for quantitative analysis.
Step-by-step explanation:
A dataset attribute that is not identifiable but constitutes data about the individual that needs to be protected is known as a Quasi-identifier. A Quasi-identifier in itself might not be sufficient to identify a person, but when combined with other quasi-identifiers, it could potentially be linked to an individual. These are contrasts to sensitive columns which contain information that explicitly identifies or provides sensitive information about the individual — such as Social Security numbers or medical records.
Using secondary data is considered nonreactive or nonrestrictive, meaning the data collection process does not influence or alter the behavior of the subjects being studied.
Qualitative data such as hair color, blood type, or ethnic group are examples of categorical data that are used to categorize or describe attributes of a population. These kinds of data are well-suited for classification but are less amenable to mathematical analysis, unlike quantitative data which can be easily processed using statistical methods.