I built HIVENET to fulfill the "Senior Project Requirement" at Stanford University under the supervision of my former professor, David Mazières. HIVENET proposes a design for a decentralized StumbleUpon replacement, provides a simulation environment to test this design, and provides a fully functional implementation of this design.
Full Disclosure: The code in this repository should be considered "pre-alpha" and could still have a number of bugs. If you see an issue, make a pull request!
To avoid software packaging issues, I built a Docker image containing a properly
configured "agent" (terminology further explained in attached technical
specification). This image has been uploaded to the Docker Hub under the name
patrickogrady/hivenet. If you haven't yet installed Docker, instructions can
be found here: https://docs.docker.com/install/
To test the underlying algorithms in HIVENET, I created a simulation environment that allows anyone to observe how parameter changes affect HIVENET performance. To simulate HIVENET with the standard parameters please run:
docker run -t -p 3000:3000 -p 4001:4001 -v `pwd`:/var/log/hivenet patrickogrady/hivenet:latest bash -c "npm test"
This command starts a Docker container to run the simulation and starts a
web server that provides a GUI view of the simulation that is available at:
localhost:3000.
An overview of simulation parameters and assumptions is provided in the technical specification.
To run modified simulations, download this GitHub repo and build a new docker image after you've made changes.
A legend for the meanings of different colors in the following diagram is provided in the technical specification.
This simulation required a user to have provided 5 units of reputation before
considering any recommendations from peers. This sufficiently prevented the
spread of suspicious URLs from malicious hosts (viewing suspicious URL
indicated by green node color).
Inspired by the BitTorrent notion of randomly unchoking peers, this simulation
explored the notion of randomly trying observed URLs when no recommendations
were available (no URL was deemed safe). This approach led to significant spread
of suspicious URL.
This simulation explored the notion of not having a minimum observed reputation
before considering peer reputation. This exposed some users to suspicious URLs
because there is a chance that a malicious peer could discover a single useful
URL, but the probability this single useful rating affects recommendations goes
down with the requirement to observe X other useful ratings before considering.
This assumes that the Docker image provided earlier is already downloaded.
To interface with HIVENET, the user can use the API endpoint that the "agent" provides at port 3000 (API reference provided in technical specification) or use the provided Chrome Extension.
To download the Chrome Extension, click here.
Once downloaded, unpack the ZIP file into a new folder (usually this just means clicking on the file on Mac).
Next, navigate to chrome://extensions. Toggle the developer mode tab in the
top right of the screen:
In the top lefthand corner of the screen click Load unpacked:
Select the file you just unzipped:
Create a folder that will store all the config files used during execution. (Docker is stateless so any files left in the container at the end of execution are deleted. So, the user must share a volume with the container where all stateful information is stored.)
After cd into this created folder, start the "agent" by running:
docker run -t -p 3000:3000 -p 4001:4001 -v `pwd`:/var/log/hivenet patrickogrady/hivenet:latest bash -c "npm start"
Open the HIVENET Extension and give it a try!:
