AnonymizationOntology

Represents the software system for anonymization, with packages and modules to apply privacy methods to datasets."

This class represents the features of a dataset, defining the properties and characteristics of the data contained within the dataset.

This class represents the transformation applied to a attribute types, indicating whether the attribute is modified or left unmodified during the anonymization process. It has only three instances 'modified','unmodified' and 'removed'.

This class represents the different types of attributes, such as sensitive, insensitive, identifying, or quasi-identifying, that can be associated with dataset attributes.

Average riskk controls the average re-identification risk of data.

This privacy model is aims to overcome limitations of prior models by restricting the relative maximal distance between distributions of sensitive attribute values, also considering positive and negative information gain.

This class represents the constant values associated with privacy models, specifically for Beta likeness. These values determine thresholds or parameters used to control privacy risk and the degree of data transformation or anonymization applied.

This class represents the maximum dimension settings for a channel in EEG recordings, specifying the upper limits on the data that can be captured for each channel.

This class represents the minimum dimension settings for a channel in EEG recordings, specifying the lower limits on the data that must be captured for each channel.

This class represents the labels assigned to channels in EEG recordings, indicating the specific functionality or type of each channel.

This class represents the physical dimensions of channels in EEG recordings, detailing the physical characteristics and specifications related to each channel.

This class represents the maximum physical dimensions of channels in EEG recordings, defining the upper limits of the physical characteristics for each channel.

This class represents the minimum physical dimensions of channels in EEG recordings, defining the lower limits of the physical characteristics for each channel.

This class represents the preferred filter settings for channels in EEG recordings, specifying the filtering methods applied to the data collected from each channel.

This class represents the recording size specifications for channels in EEG recordings, detailing the amount of data recorded for each channel during a session.

This class encompasses specific information related to the channels in EEG recordings, including various attributes that describe the characteristics, settings, and configurations of each channel.

This class represents the classification levels used to categorize the privacy risk category of attributes types, privacy impact of privacy models and reidentification risk of privacy models in the context of anonymization.

This class represents a collection of data, for analysis or processing, which may include various types of information such as metadata, and associated attributes. It represents a group of datasets that are logically related or organized together.

A class representing different data types supported by the anonymization process, such as integer, float, string, or more complex data types.

A class representing different data formats for datasets supported by the anonymization process, such as CSV, JSON, or XML, indicating the structure in which data is stored.

Privacy model to prevent sensitive attributes disclosure in data anonymization. The goal is to ensure that the information about individuals cannot be inferred with high confidence.

This model is used to protect from data linkage.

This class represents the constant values associated with the delta. Delta thresholds for disclosure risk in the delta Disclosure and delta Presence models.

A privacy model that provides privacy by ensuring that the probability of output of the anonymization process does not change much if data of an individual is added to or removed .

A class representing different types of domains supported by the anonymization process, such as healthcare, for definining the context in which the dataset is used.

This class represents the identifier for EEG recordings, uniquely distinguishing each recording session and facilitating the organization and retrieval of EEG data.

This class represents the compliance of a privacy method with the General Data Protection Regulation (GDPR). It has only two instances to define whether a particular privacy model is GDPR compliant or not.

This class represents the general header information associated with EEG datasets, encompassing various attributes that provide metadata about the recording, such as start date, duration, and signal details.

This class represents the duration of the recording in the general header information of EEG datasets, specifying the length of time for which the data was collected.

This class represents the file type information in the general header of EEG datasets, indicating the format of the data file and its compatibility with various processing tools.

This class represents the header size in the general header information of EEG datasets, specifying the amount of data allocated for the header in the file.

This class represents the number of records in the general header information of EEG datasets, specifying the total count of individual data records contained within the file.

This class represents the number of signals recorded in the general header information of EEG datasets, indicating how many distinct channels of data were captured during the recording session.

This class represents reserved space in the general header information of EEG datasets, which may be allocated for future use or to accommodate specific data formats.

This class represents the start date of the EEG recording in the general header information, indicating when the recording session commenced.

This class represents the start time of the EEG recording in the general header information, specifying the exact time when the recording session began.

This class represents the generalization model, used to reduce the granularity of data to protect privacy.

Privacy model for making data k-anonymous. It ensures each record is indistinguishable from at least k-1 other records.

This class represents the constant value 'k' used in k-anonymization, specifying the number of records that must be indistinguishable to ensure privacy.

L-Diversity enhances k-anonymization. It ensures that each equivalence class has at least l distinct values for sensitive attributes.

This class represents the constant value 'l' used in l-diversity, specifying the minimum number of distinct sensitive attribute values required in each equivalence class.

This class represents the local recording information associated with EEG datasets, detailing attributes that describe the specific characteristics and settings of the recording environment.

This class represents the machine used for EEG recordings, specifying details about the equipment and its configurations that affect the data collection process.

Represents several modules within a package, each performing specific functions in the context of data anonymization or processing.

This class represents the module dedicated to the anonymization process within a software package.

This class represents a module within the software that specifically handles datasets.

This class represents the constant value used in noise addition techniques, specifying the amount of noise to be introduced into the data to protect privacy.

Represents a unit within the software, in this case, the anonymization tool, containing one or more modules that implement specific functionalities.

This class represents the age of the patient associated with EEG recordings, providing crucial demographic information that may influence the analysis and interpretation of the data.

This class represents the date of birth of the patient associated with EEG recordings, serving as an important demographic attribute for identifying and categorizing patients.

This class represents the full name of the patient associated with EEG recordings, providing essential identification information for the patient within the dataset.

This class represents the gender of the patient associated with EEG recordings, serving as an important demographic attribute that may be relevant for data analysis and interpretation.

This class represents the unique identifier for the patient associated with EEG recordings, ensuring accurate tracking and management of patient data within the dataset.

This class represents the patient-specific information associated with EEG datasets, detailing attributes that pertain to the individual patient, such as demographics, medical history, and other relevant data.

This privacy model is used to restrict the fraction of records which are unique within the dataset.

Represents the potential risks associated with a privacy model, such as reidentification, data loss, or information leakage.

Represents a privacy model used in GDPR compliant anonymization.

Represents constant values associated with privacy models, such K value for K-Anonymization, used to control data anonymization.

This class represents the start date of the local recording session in EEG datasets, indicating when the data collection began.

This class represents the label associated with the start date of the local recording session in EEG datasets, providing a human-readable reference for the date when data collection commenced.

This class represents the suppression model, used to hide or remove data in order to protect sensitive information during anonymization.

tClosenees enhances l-diversity by ensuring that the distribution of a sensitive attributse in any equivalence class is close to the distribution of other dataset attributes in the overall dataset.

This class represents the constant value 't' used in t-closeness, specifying the level of closeness required between the distribution of sensitive attributes in equivalence classes and the overall dataset.

This class represents the technology or technique used in EEG recordings, detailing the methods and tools applied during the data collection process.

Methods used to transform data to achieve anonymization. It includes techniques like generalization and suppression that modify data to anonymize it.

Measures the degree to which the utility of the data is preserved post anonymization. Utility ensures that anonymous data remains useful for research purposes.

The class represents a specific set of data items, or records. It is the fundamental unit of data within a collection and contains the actual data points or entities. It represents the dataset to be used for GDPR compliant dataset. In our case it is individual EEG session recording, collected during a specific recording session at Temple University.