{"repo":"IFCA-Advanced-Computing/anjana","free":true,"listed":false,"github":"https://github.com/IFCA-Advanced-Computing/anjana","clone":"git clone https://github.com/IFCA-Advanced-Computing/anjana.git","description":"ANJANA is a Python library for anonymizing sensitive data","language":"Python","stars":49,"topics":["anonymity","data-privacy","k-anonymity","l-diversity","privacy","t-closeness","data-analytics","data-anonymization","data","databases"],"license":"Apache-2.0","category":"self-hosted-apps","readme_excerpt":"Anonymity as major assurance of personal data privacy ANJANA is a Python library for anonymizing sensitive data. The following anonymity techniques are implemented, based on the Python library pyCANON : k-anonymity . (α,k)-anonymity . ℓ-diversity . Entropy ℓ-diversity . Recursive (c,ℓ)-diversity . t-closeness . Basic β-likeness . Enhanced β-likeness . δ-disclosure privacy . Installation First, we strongly recommend the use of a virtual environment. In linux: Using pip : Install anjana (linux and windows): Using git : Install the most updated version of anjana (linux and windows): Getting started For anonymizing your data you need to introduce: The pandas dataframe with the data to be anonymized. Each column can contain: identifiers, quasi-indentifiers or sensitive attributes. The list with the names of the identifiers in the dataframe, in order to suppress them. The list with the names of the quasi-identifiers in the dataframe. The sentive attribute (only one) in case of applying other techniques than k-anonymity . The level of anonymity to be applied , e.g. k (for k-anonymity ), ℓ (for ℓ-diversity ), t (for t-closeness ), β (for basic or enhanced β-likeness ), etc. Maximum level of record suppression allowed (from 0 to 100, acting as the percentage of suppressed records). Dictionary containing one dictionary for each quasi-identifier with the hierarchies and the levels. Example: apply k-anonymity , ℓ-diversity and t-closeness to the adult dataset with some predefined hierarc","default_branch":null,"files":null,"tree":[],"storefront":"/r/IFCA-Advanced-Computing","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/IFCA-Advanced-Computing/anjana/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}