Hướng dẫn data anonymization python github
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# Show python-data-anonymizationHere is 1 public repository matching this topic...
Python Data Anonymization & Masking Library For Data Science Tasks
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Context aware, pluggable and customizable data protection and anonymization SDK for text and images
Python Data Anonymization & Masking Library For Data Science Tasks
This repository allows you to anonymize sensitive information in images/videos. The solution is fully compatible with the DL-based training/inference solutions that we already published/will publish for Object Detection and Semantic Segmentation.
Examples scripts that showcase how to use Private AI Text to de-identify, redact, hash, tokenize, mask and synthesize PII in text.
Library for identification, anonymization and de-anonymization of PII data
Implementation of An Efficient Clustering Method for k-Anonymization in Python 2.7
Anonymize your Pandas data. Preserve privacy.
Anonymize data using AES-128 encryption/decryption algorithm.
A fully responsive, full stack web application with a working login system designed to demonstrate the benefits of password hashing, salting, and data anonymization.
Data anonymization signals for Tortoise ORM.
M.Tech final year project to create a data anonymization tool.
Improve this pageAdd a description, image, and links to the data-anonymization topic page so that developers can more easily learn about it. Curate this topic Add this topic to your repoTo associate your repository with the data-anonymization topic, visit your repo's landing page and select "manage topics." Learn more A possible solution to dealing with Personal identifying information(PII) in the datasets is to anonymize the dataset by replacing information that would identify a real individual with information about a fake (but similarly behaving or sounding) individual. Given a target dataset (for example, a CSV file with multiple columns),
produce a new dataset such that for each row in the target, the anonymized dataset does not contain any personally identifying information. The anonymized dataset should have the same amount of data and maintain its analytical value.
Tools:There are two third-party libraries for generating fake data with Python
Faker provides anonymization for user profile data, which is completely generated on a per-instance basis. Fake Factory uses a providers approach to load many different fake data generators in multiple languages (deprecated now - still useable) References:
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