# STRM Privacy Diagnostics
This package contains diagnostics for your data, by means of computing k-Anonymity, l-Diversity and t-Closeness.
You can compute the scores by passing your data and indicating which columns are quasi-identifiers and sensitive attributes.
A 'quasi identifier' is a data attribute on an individual that together with other attributes could identify them. E.g. your length probably doesn't discern you from a larger group of people, but the combination of your length, age and city of birth will if someone has some knowledge about you.
A 'sensitive attribute' is a sensitive data point, like a specific medical diagnosis or credit score.
## Installation
Install the package via Pip:
```
pip install strmprivacy-diagnostics
```
## Usage
Simply import the package and
* point it to your input data
* calculate the statistics by passing the quasi identifiers and sensitive attributes
* print a report by passing the quasi identifiers and sensitive attributes
```python
from strmprivacy.diagnostics import PrivacyDiagnostics
# create an instance of the diagnostics class
d = PrivacyDiagnostics("/path/to/csv")
# calculate the statistics
d.calculate_stats(
qi=['qi1', 'qi2', ...], # names of quasi identifier columns,
sa=['sa1', 'sa2', ...], # names of sensitive attributes
)
# create report
d.create_report(
qi=['qi1', 'qi2', ...], # names of quasi identifier columns,
sa=['sa1', 'sa2', ...], # names of sensitive attributes
)
d.stats
>>> {'k': xxx, 'l': {'col1': xxx, ...}, 't': xxx}
```
Raw data
{
"_id": null,
"home_page": "",
"name": "strmprivacy-diagnostics",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "",
"keywords": "strmprivacy privacy diagnostics",
"author": "Stream Machine B.V.",
"author_email": "apis@strmprivacy.io",
"download_url": "https://files.pythonhosted.org/packages/64/64/d4e5add793c085d29a7e3570d7f6fca16c8073894c852b90e849796e5f7a/strmprivacy-diagnostics-1.2.2.tar.gz",
"platform": null,
"description": "# STRM Privacy Diagnostics\n\n\nThis package contains diagnostics for your data, by means of computing k-Anonymity, l-Diversity and t-Closeness.\n\nYou can compute the scores by passing your data and indicating which columns are quasi-identifiers and sensitive attributes. \n\nA 'quasi identifier' is a data attribute on an individual that together with other attributes could identify them. E.g. your length probably doesn't discern you from a larger group of people, but the combination of your length, age and city of birth will if someone has some knowledge about you.\n\nA 'sensitive attribute' is a sensitive data point, like a specific medical diagnosis or credit score. \n\n## Installation\nInstall the package via Pip:\n\n```\npip install strmprivacy-diagnostics\n```\n\n## Usage\nSimply import the package and\n* point it to your input data\n* calculate the statistics by passing the quasi identifiers and sensitive attributes\n* print a report by passing the quasi identifiers and sensitive attributes\n\n```python\nfrom strmprivacy.diagnostics import PrivacyDiagnostics\n\n# create an instance of the diagnostics class\nd = PrivacyDiagnostics(\"/path/to/csv\")\n\n# calculate the statistics\nd.calculate_stats(\n qi=['qi1', 'qi2', ...], # names of quasi identifier columns,\n sa=['sa1', 'sa2', ...], # names of sensitive attributes\n)\n\n# create report\nd.create_report(\n qi=['qi1', 'qi2', ...], # names of quasi identifier columns,\n sa=['sa1', 'sa2', ...], # names of sensitive attributes\n)\n\nd.stats\n>>> {'k': xxx, 'l': {'col1': xxx, ...}, 't': xxx}\n```\n\n\n",
"bugtrack_url": null,
"license": "",
"summary": "STRM Privacy Diagnostics for reporting privacy metrics on a dataset.",
"version": "1.2.2",
"split_keywords": [
"strmprivacy",
"privacy",
"diagnostics"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "d517ee256caeb2c96cafe40cb1240f490beecb6090e2d99059b7121ad091577f",
"md5": "e54ceddd4edb21f091715765d97dfec2",
"sha256": "6251800ba6282cfe5a0f6e662fe4066fa58b95ed24815e9ac012817a9b46a415"
},
"downloads": -1,
"filename": "strmprivacy_diagnostics-1.2.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "e54ceddd4edb21f091715765d97dfec2",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 8992,
"upload_time": "2023-01-25T15:02:45",
"upload_time_iso_8601": "2023-01-25T15:02:45.477153Z",
"url": "https://files.pythonhosted.org/packages/d5/17/ee256caeb2c96cafe40cb1240f490beecb6090e2d99059b7121ad091577f/strmprivacy_diagnostics-1.2.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "6464d4e5add793c085d29a7e3570d7f6fca16c8073894c852b90e849796e5f7a",
"md5": "e043bc3fb36e36e9dd9330cd211b9b33",
"sha256": "548a6896288ffc6e232cbb72b14d1808d45f55d491206cc5f96576501f5e091f"
},
"downloads": -1,
"filename": "strmprivacy-diagnostics-1.2.2.tar.gz",
"has_sig": false,
"md5_digest": "e043bc3fb36e36e9dd9330cd211b9b33",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 8525,
"upload_time": "2023-01-25T15:02:46",
"upload_time_iso_8601": "2023-01-25T15:02:46.572922Z",
"url": "https://files.pythonhosted.org/packages/64/64/d4e5add793c085d29a7e3570d7f6fca16c8073894c852b90e849796e5f7a/strmprivacy-diagnostics-1.2.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-01-25 15:02:46",
"github": false,
"gitlab": false,
"bitbucket": false,
"lcname": "strmprivacy-diagnostics"
}
JSON