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Add taxonomy to classify the degree of identifiability of personal data

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circlsupportuser 2018-05-02 17:11:31 +02:00
parent 9f8c68bb41
commit a8619b240d
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{
"namespace": "fpf",
"description": "The Future of Privacy Forum (FPF) [visual guide to practical de-identification](https://fpf.org/2016/04/25/a-visual-guide-to-practical-data-de-identification/) taxonomy is used to evaluate the degree of identifiability of personal data and the types of pseudonymous data, de-identified data and anonymous data. The work of FPF is licensed under a creative commons attribution 4.0 international license.",
"version": 0,
"predicates": [
{
"value": "degrees-of-identifiability",
"expanded": "Degrees of identifiability",
"description": "Information containing direct and indirect identifiers."
},
{
"value": "pseudonymous-data",
"expanded": "Pseudonymous Data",
"description": "Information from which direct identifiers have been eliminated or transformed, but indirect entifiers remain intact."
},
{
"value": "de-identified-data",
"expanded": "De-identified data",
"description": "Direct and known indirect identifiers have been removed or manipulated to break the linkage to real world identities."
},
{
"value": "anonymous-data",
"expanded": "Anonymous data",
"description": "Direct and indirect identifiers have en removed or manipulated together with mathematical and technical guarantees to prevent re-identification."
}
],
"values": [
{
"predicate": "degrees-of-identifiability",
"entry": [
{
"value": "explicitly-personal",
"expanded": "Explicitly personal",
"description": "Name, address, phone number, SSN, government-issued ID (e.g., Jane Smith, 123 Main Street, 555-555-5555)"
},
{
"value": "potentially-identifiable",
"expanded": "Potentially identifiable",
"description": "Unique device ID, license plate, medical record number, cookie, IP address (e.g., MAC address 68:A8:6D:35:65:03)"
},
{
"value": "not-readily-identifiable",
"expanded": "Not readily identifiable",
"description": "Same as Potentially Identifiable except data are also protected by safeguards and controls (e.g., hashed MAC addresses & legal representations)"
}
]
},
{
"predicate": "pseudonymous-data",
"entry": [
{
"value": "key-coded",
"expanded": "Key coded",
"description": "Clinical or research datasets where only curator retains key (e.g., Jane Smith, diabetes, HgB 15.1 g/dl = Csrk123)"
},
{
"value": "pseudonymous",
"expanded": "Pseudonymous",
"description": "Unique, artificial pseudonyms replace direct identifiers (e.g., HIPAA Limited Datasets, John Doe = 5L7T LX619Z) (unique sequence not used anywhere else)"
},
{
"value": "protected-pseudonymous",
"expanded": "Protected pseudonymous",
"description": "Same as Pseudonymous, except data are also protected by safeguards and controls"
}
]
},
{
"predicate": "de-identified-data",
"entry": [
{
"value": "de-identified",
"expanded": "De-identified",
"description": "Data are suppressed, generalized, perturbed, swapped, etc. (e.g., GPA: 3.2 = 3.0-3.5, gender: female = gender: male)"
},
{
"value": "protected-de-identified",
"expanded": "Protected de-identified",
"description": "Same as De-Identified, except data are also protected by safeguards and controls"
}
]
},
{
"predicate": "anonymous-data",
"entry": [
{
"value": "anonymous",
"expanded": "Anonymous",
"description": "For example, noise is calibrated to a data set to hide whether an individual is present or not (differential privacy)"
},
{
"value": "aggregated-anonymous",
"expanded": "Aggregated anonymous",
"description": "Very highly aggregated data (e.g., statistical data, census data, or population data that 52.6% of Washington, DC residents are women)"
}
]
}
]
}