Sensitive Data Management: Data Discovery and Anonymization toolkit
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Updated
Aug 11, 2018 - Java
Sensitive Data Management: Data Discovery and Anonymization toolkit
📛 An experimental data anonymisation library
Distributed Anonymization Platform for SQL databases
Hash data stored in Excel spreadsheet using pandas and Python's hashlib library
DPP - "Anonymizing Transaction Databases for Publication" - AA 2022/23
A selected collection of my work samples
A project for generating data defined by a data definition file, the data would be a representation of real data that would be expected. Also enables the anonymisation of Personal identifiable information of data provided in either CSV or SQL connection.
Neuralyzer is a library and a command line tool to anonymize databases (by updating existing data or populating a table with fake data)
Anonymizing confidential data using the concept of masking.
Exploring US Census microdata, tackling privacy issues, and anonymization. Exercise A delves into quasi-identifiers, anonymization methods, identification risks, and differential privacy. Exercise B involves data loading, k-anonymity, histograms, adding noise for privacy, computing private averages, and analyzing privacy parameter impacts.
Anonymizer: scrambles your confidential production data for use in test environments
MATLAB code for extracting, converting and anonymising files in CTF MEG proprietary format.
Extracting and analyzing user data stored by Facebook using Python and SQLIte
Anonymize data using AES-128 encryption/decryption algorithm.
This repository contains an analysis of the US Census Bureau's microdata from the 2010 census. The current analysis focuses on understanding the privacy threats associated with the non-anonymized dataset and exploring techniques to preserve privacy while analyzing the data.
ARX is a comprehensive open source data anonymization tool aiming to provide scalability and usability. It supports various anonymization techniques, methods for analyzing data quality and re-identification risks and it supports well-known privacy models, such as k-anonymity, l-diversity, t-closeness and differential privacy.
ANJANA is a Python library for anonymizing sensitive data
Runner Up AIML - HackToFuture, SJEC, Mangalore (Rs. 20,000)
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