Many people are unable to cope with the various privacy notices that we have to deal with on a daily basis. It is important to manage your data and online identity and know how these informations are processed and used. 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.
We are also interested to get an overview of privacy preferences. If you like, you can fill out the form. If you are interested in the results yourself, you can leave your spam-mail adress in the last question, we will send you the link with the summary results:
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.
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.
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.
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
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.
Further reading, in no particular order.Online policy tools Machine learning Policy documentation
This is a companion discussion topic for the original entry at https://hacknight.dinacon.ch/project/20.html