Administrative Data are data collected about people when they interact with public services. 

Aggregate Data are data combined from several measurements or individuals. When data are aggregated, groups of observations are replaced with summary statistics based on those observations.

The adjective "anonymised" is used to describe data that has undergone a process of manipulation—such as the removal of direct identifiers, aggregation, or suppression—to ensure it no longer relates to an identified or identifiable person. This means that truly anonymised data cannot be linked back to the original individual through any reasonable means. For this reason, anonymised data is no longer considered personal data and is no longer subject to data protections laws like the GDPR. 

As defined in Article 4 of the GDPR, a Controller is a natural or legal person, who is responsible for defining the purposes and means of data processing.
 

The Article 4 of the GDPR outlines that a Data Breach occurs where ‘there has been the accidental or unlawful destruction, loss, alteration, unauthorised disclosure of, or access to, personal data transmitted, stored or otherwise processed’. 

A Data Protection Impact Assessment (DPIA) is a formal process used to identify, assess, and minimise the privacy risks associated with data processing activities. Under the GDPR, a DPIA is legally mandatory before starting any new research or project that is likely to result in a high risk to the rights and freedoms of individuals (such as large-scale monitoring or processing sensitive health data).

A Data Subject is the observation about whom the data relates. 

Usually a Data Subject is a natural person, but in the context of ASSURED, we expand this definition to refer to an observation that is represented by a single row in microdata. Under this definition, a Data Subject could also be a commercial entity, a group of people such as a family, a sensitive habitat, etc. 

To ensure the clarity and readability of the text, we tend to refer to the Data Subject as if they were a natural person (i.e. referring to Data Subjects as 'they') but it should be understood that the principles expressed also apply to other types of Data Subjects.   

This is the idea that one observation could account for most of the value in a statistical measure, and therefore be identifiable. It can sometimes apply to individuals,but is more of a concern for business statistics where firms might dominate a market or sector, or might be making large investments in a particular year.

Source: SDC Handbook 2019, V1: https://securedatagroup.org/guides-and-resources/sdc-handbook/

When used in reference to data: identifiable data are all types of data or any piece of information that can be used to identify or reidentify an individual.

It can be also used in reference to a person. Particulary in legal texts, it appears in the following combination "identifiable natural person". From the legal perspective, an identifiable natural person is a living individual who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person. 

Microdata refers to raw, unit-level data collected from individuals, households, or businesses via surveys, censuses, or administrative records. It contains detailed, unaggregated information about specific entities and is used to create custom tabulations, perform in-depth analysis, and generate statistics.