Privacy Preserving Data Stream Perturbation

This repository contains experimentation with combinations of random projection, translation, and additive noise as a method for performing privacy-preserving data stream mining (tested against known input-output attacks) in an online learning context.

Quickstart

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. Note that the results of each experiment are saved to disk to prevent the need to re-execute the experiments when re-viewing an experiment's results.

Dependencies

• Java (>= 1.8.0)
• Leiningen (>= 2.0)

Running Tests

make run-tests

Tests can also be run repeatedly from a Clojure REPL:

1. lein repl
2. (use 'midje.repl)
3. (autotest)

Further Usage

See Makefile commands