Many people like you and I feel that we are unable to cope with all the various privacy notices we have to deal with on a daily basis. Our data is important to us, and indeed defines our online identity.
Your Privacy Butler will help you to understand any privacy notices. Simply tell Privacy Butler which data processing is a “no go” for you. It converts the privacy notice into icons that show you immediately whether your desired data protection standard is met or not.
This project was started at the Swiss Legal Tech 2018 hackathon in Zürich, Switzerland. The original challenge idea can be found here.
You can see a screencast of the working demo here
The backend uses Java with Spring Boot 2 and communicates with the Google Cloud Natural Language API. You have to create the credentials yourself in order to be able to communicate with Google Cloud.
Find the backend project files here and instructions to get started in the legal-hackathon-backend folder.
The frontend uses Typescript with Angular 6 and Material Design as a styling framework addition to Angular.
You can find the frontend project files and build instructions in legal-hackathon-frontend.
There is also a static HTML launch page defined in index.html with resources in the web folder. The design template used is HTML5 UP, with jQuery and FontAwesome.
Check the markup for an Easter egg..and a modest proposal on using schema in META tags to publish web site policy in machine readable form. See our reading list below.
We used the Google Cloud Natural Language API in this project for rapid analysis of policy texts. See Quickstart, NL Samples, and Java samples for Google Cloud Platform.
You will need to obtain a developer key from the Cloud API console to use our current backend.
We also ran a short machine learning classification experiment using an open dataset of opt-out policies from usableprivacy.org in the Keras.io deep learning environment. The results can be seen in a Python notebook made with Jupyter, in the ml subfolder.
The dataset used in the experiment above was one of the ones recommended by Pribot.org, a project that was a major motivation for our work here. Many thanks to Dr. Harkous for feedback to our concept during the hackathon.
We also considered using IBM Watson (see Fredrik Stenbeck comparison - and OpenNLP at Apache.
Further reading, in no particular order.
- https://pribot.org/
- https://tosdr.org/
- https://chrome.google.com/webstore/detail/privacycheck/poobeppenopkcbjejfjenbiepifcbclg?hl=en-US
- https://medium.com/datathings/the-magic-of-lstm-neural-networks-6775e8b540cd
- http://blog.echen.me/2017/05/30/exploring-lstms/
- https://github.com/ar-ms/lstm-mnist
- https://openscience.adaptcentre.ie/projects/GDPRtEXT/
- https://www.w3.org/community/dpvcg/
- https://ebiquity.umbc.edu/resource/html/id/370/Ontology-for-Data-Privacy-Policy
- http://www.semantic-web-journal.net/content/improving-readability-online-privacy-policies-through-doop-domain-ontology-online-privacy