This repository contains experimentation with variations of cumulative noise additions performing privacy-preserving data stream mining. Adaptive random forest for the classification and known I/O attacks to measure the privacy has been used. The objective of this work is to control the maximum noise level of cumulative noise addition by in- cooperating different techniques.
If you have Docker installed, you can run the experiments contained
within this codebase by executing make jupyter
, opening the returned
URL in a web browser, and executing the contents of the provided
Jupyter notebooks (This has only been tested on an Ubuntu 16.04 host
running Docker 17.05.0-ce).
You will need to run the notebooks in the "dataset-construction" sub-folder before the notebooks that depend on those datasets.
- Java (>= 1.8.0)
- Leiningen (>= 2.0)
See Makefile commands