UPA

UPA is a big-data system that automatically infers a local sensitivity value for enforcing Individual Differential Privacy. Below shows a simple example demonstrating the functionalities of UPA.

Core dependencies

sudo apt-get insall openjdk-8-jdk maven

How to build UPA

UPA is built in the same way as Apache Spark Assuming that you have cloned this repo to your home directory ($HOME/UPA) i.e., running:

cd $HOME/UPA
build/mvn -DskipTests -T 40 package

Running an example

1.Generate a sample dataset:

mkdir $HOME/test; python gen_data.py --wq simple --path $HOME/test/dataset.txt --s 100000

This will create a sample dataset of 100000 records under $HOME/test/dataset.txt.

2.Parition the dataset:

python indexing.py --wq index --path $HOME/test/dataset.txt

This will partition the dataset ($HOME/test/dataset.txt) into two partitions, the partitioned dataset is located in $HOME/test/dataset.txt.upa.

3.Running an example:

./demo_attack.sh

The outputs are stored in output.txt. Detailed descriptions about this attack can be found in the shell file.

Run UPA in cluster mode

First start a master by running the following command on a master computer:

./sbin/start-master.sh -h <ip address of master> -p <port to be used>

Then start workers by running the following command on a worker computer:

./sbin/start-slave.sh spark://<ip address of master>:<port to be used>

Then running ./demo_attack.sh on the master computer. Note that the input dataset has to be replicated on both master and workers. After finishing testing, stop the master and workers by running ./sbin/stop-master.sh and ./sbin/stop-slave.sh on master and worker computers respectively, to release their network resources.