The current glossary includes and defines the main terminology used in the context of the project e-SIDES:
Privacy Enhancing Technology (PET) targeted at anonymizing personal data.
Artificial intelligence (AI)
Science focusing on mimicking human intelligence.
Datasets that are so large or complex that traditional data processing technologies are inadequate to deal with them. Big data is often characterized by 3 Vs: Volume, Velocity and Variety, sometimes complemented with additional Vs, such as Veracity and Value.
Big data technologies
Information technologies for storing, processing, analyzing, disseminating and managing big data.
Big data analytics
Big data technologies focusing on analyzing big data.
The use of business information for business strategies and decision-making.
Encryption of information to prevent unauthorized people from accessing this information.
Internet-based computing in which data storage and processing resources connected to the internet are shared.
Digital archives with large amounts of data that can be searched in automated ways.
Pursuant to Article 3(8), it is means the competent authority which, alone or jointly with others, determines the purposes and means of the processing of personal data.
Data coupling/Data transfers
Coupling personal data files of individuals from different databases (e.g., financial data, health data, passenger data, etc.) in order to obtain a more complete picture of that person.
Process of transforming unstructured datasets and/or data from heterogeneous sources into structured, homogeneous formats.
Automatic analysis of data using mathematical algorithms, in order to find new patterns and relations in data.
Using data several times for the same purpose in the same context
Using data for different purposes than for which they were initially collected, but still in the same context as the original purpose.
Using data in another context than in which they were initially collected.
System for storing, reporting and analyzing data.
Deterministic record linkage
Entity resolution in which a minimum number of identifying data has to match.
Discrimination-aware data mining
Data mining technologies with built-in requirements (based on privacy by design) to avoid discovery of discriminating patterns.
Determining which persons are the same in different datasets.
Automated processing and analysis of large amounts of camera footage, for instance, with the use of facial recognition or pattern recognition.
Systems and applications for collecting, storing, processing, disseminating and managing data.
Internet of Things
Internet of connected smart devices, including vehicles, buildings and electronic devices.
Filters can be used to scan data input and decide data output. Filters can be based on norms, profiles, models, blacklists, etc.
Entity resolution using both identifying and non-identifying data.
Area in computer science that focuses on enabling computers to learn without being explicitly programmed.
Analysis to clarify relations between people, including ‘who is who?’ (entity resolution) and ‘who knows whom?’
Technologies for automated data analysis. See also data mining.
Privacy by design
Approach in which privacy requirements are taken into account throughout the entire engineering process.
Privacy Enhancing Technologies (PETs)
Technologies that protect and improve privacy, particularly technologies that comply with privacy and data protection laws.
Privacy-preserving data mining
Data mining technologies with built-in requirements (based on privacy by design) to preserve privacy.
Probabilistic record linkage
See: Fuzzy matching.
Ascribing characteristics to individuals or groups of people on the basis of available data and results of data analyses.
See: Entity resolution.