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e-SIDES Glossary

The current glossary includes and defines the main terminology used in the context of the project e-SIDES:

Access and portability
Facilitating the use and handling of data in different contexts.

Access control
Selective restriction of access to places or resources.

Accountability
Evaluation of compliance with policies and provision of evidence.

Anonymisation
Encryption or removal of personally identifiable information.

Anonymiser
Privacy Enhancing Technology (PET) targeted at anonymizing personal data.

Artificial Intelligence (AI)
Science focusing on mimicking human intelligence.

Big data
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.

Business intelligence
The use of business information for business strategies and decision-making.

Cryptography
Encryption of information to prevent unauthorized people from accessing this information.

Cloud computing
Internet-based computing in which data storage and processing resources connected to the internet are shared.

Databases
Digital archives with large amounts of data that can be searched in automated ways.

Data controller
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.

Data fusion
Process of transforming unstructured datasets and/or data from heterogeneous sources into structured, homogeneous formats.

Data mining
Automatic analysis of data using mathematical algorithms, in order to find new patterns and relations in data.

Data provenance
Attesting of the origin and authenticity of the information.

Data recycling
Using data several times for the same purpose in the same context.

Data repurposing
Using data for different purposes than for which they were initially collected, but still in the same context as the original purpose.

Data recontextualization
Using data in another context than in which they were initially collected.

Data warehouses
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.

Encryption
Encoding of information so that only authorised parties can access it.

Entity resolution
Determining which persons are the same in different datasets.

Image processing
Automated processing and analysis of large amounts of camera footage, for instance, with the use of facial recognition or pattern recognition.

Information technology
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
Filters can be used to scan data input and decide data output. Filters can be based on norms, profiles, models, blacklists, etc.

Fuzzy matching
Entity resolution using both identifying and non-identifying data.

Machine learning
Area in computer science that focuses on enabling computers to learn without being explicitly programmed.

Multi-party computation
Distribution of data and processing tasks over multiple parties.

Network analysis
Analysis to clarify relations between people, including ‘who is who?’ (entity resolution) and ‘who knows whom?’

Pattern recognition
Technologies for automated data analysis. See also data mining.

Policy enforcement
Enforcement of rules for the use and handling of resources.

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.

Profiling
Ascribing characteristics to individuals or groups of people on the basis of available data and results of data analyses.

Record linkage
See: Entity resolution.

Sanitisation
Encryption or removal of sensitive information.

Transparency
Explication of information collection and processing.

User control
Specification and enforcement of rules for data use and handling.


This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731873. The views expressed on this website are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Copyright belongs to the authors of this website. Use of any materials from this website should be referenced and is at the user's own risk.