Feature Extraction

A central focus of the Tracking the Trackers project has been to find simple ways to detect whether a given Android APK app file contains code which tracks the user. The ideal scenario is a simple program that can scan the APK and tell a non-technical user whether it contains trackers, but as decades of experience with anti-virus and malware scanners have clearly demonstrated, scanners will always contain a large degree of approximation and guesswork. [Read More]

There is a new story to add to the list of horrors of Surveillance Capitalism: the United States’ Military is purchasing tracking and location data from companies that track many millions of people. We believe the best solution starts with making people aware of the problem, with tools like Exodus Privacy. Then they must have real options for stepping out of “big tech”, where tracking dominates. F-Droid provides Android apps that are reviewed for tracking and other “anti-features”, and F-Droid is built into mobile platforms like CalyxOS that are free of proprietary, big tech software. [Read More]

As part of the Tracking the Trackers project, we are inspecting thousands of Android apps to see what kinds of tracking we can find. We are looking at both the binary APK files as well as the source code. Source code is of course easy to inspect, since it is already a form that is meant to be read and reviewed by people. Android APK binaries are a very different story. [Read More]

One key component of the Tracking the Trackers project is building a machine learning (ML) tool to aide humans to find tracking in Android apps. One of the most important pieces of developing a machine learning tool is figuring out which “features” should be fed to the machine learning algorithms. In this context, features are constrained data sets derived from the whole data set. In our case, the whole data set is terabytes of APKs. [Read More]