Kakute is the first Information Flow Tracking (IFT) system for big-data. It is built on Spark, a popular big-data processing engine in both industry and academia.
Kakute provides a unified API for adding / removing tags for data and controlling IFT across hosts. We have built several applications based on Kakute for debugging, data provenance, preventing information leakage and performance optimization.
This work has been accepted by 33th Annual Computer Security Applications Conference (ACSAC'17), and you can see our design details in this paper.
For those who would like to reproduce result in the paper, you can find the dataset in data/kakute.
apt-get install openjdk-sdk-8 openjdk-sdk-8-source maven
Download phosphor, built it and instrument Java JDK
git clone https://github.com/hku-systems/phosphor.git cd phosphor mvn clean verify
git clone https://github.com/hku-systems/kakute.git
Setup the correct phosphor directory in core/pom.xml
<dependency> <groupId>edu.columbia.cs</groupId> <artifactId>phosphor</artifactId> <version>0.0.3</version> <scope>system</scope> <systemPath>$DIRECTORY_TO_PHOSPHOR/Phosphor/target/Phosphor-0.0.3-SNAPSHOT.jar</systemPath> </dependency>
Build Kakute, it is the same as building Spark
cd kakute build/mvn -DskipTests clean package
Modify dft.conf according your configuration of phosphor.
dft-host = 127.0.0.1 // driver host ip dft-port = 8787 // driver host port dft-phosphor-java = $DIRECTORY_TO_PHOSPHOR/Phosphor/target/ dft-phosphor-jar = $DIRECTORY_TO_PHOSPHOR/Phosphor/target/Phosphor-0.0.3-SNAPSHOT.jar dft-phosphor-cache = $DIRECTORY_FOR_CACHE graph_dump_path = graph.dump dft-tracking = rule dft-input-taint = false dft-scheme = true
Congrats. You have finished building Kakute, you can try with a simple example below.
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Spark is built using Apache Maven. To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
And run the following command, which should also return 1000:
Spark also comes with several sample programs in the
To run one of them, use
./bin/run-example <class> [params]. For example:
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Testing first requires building Spark. Once Spark is built, tests can be run using:
Please see the guidance on how to run tests for a module, or individual tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.