リポジトリ
1M_Generalization is a simple anonymization algorithm for 1:M dataset. It contains two sub-algorithms: Mondrian (for relational part) and Partition (transaction part). Both of them are straight forward, and can be repalced by more powerful algorithm with limtied modification.
Algorithms Princeton exercises
Distributed Tensorflow, Keras and BigDL on Apache Spark
try to analyzing weibo data with Spark
This repository is an open source python implement for Anatomy. I implement this algorithm in python for further study.
Anatomize and Partition Anonymization
This repository is an python implement of Apriori_based_Anonymization for set-valued dataset anonymization.
ARX is a comprehensive open source data anonymization tool that has been designed from the ground up to provide high scalability and ease of use. It supports risk-based anonymization, methods for analyzing data quality and re-identification risks, as well as privacy models, such as k-anonymity, l-diversity, t-closeness and differential privacy
OpenVINO auto_optimization
The raw mondrian is designed for numerical attributes. When comes to categorical attributes, Mondrian needs to transform categorical attributes to numerical ones. This transformations is not good for some applications. In 2006, LeFevre proposed basic Mondrian, which support both categorical and numerical attributes. This repository is an implementation for basic Mondrian.
Core HW bindings and optimizations for BigDL
UI Mangement tool for openclaw fleet
cluster based generalization for k-anonymity
Confidential Computing Zoo provides confidential computing solutions based on Intel SGX, TDX, HEXL, etc. technologies.