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Submitted URL: http://fpmon.github.io/
Effective URL: https://fpmon.github.io/fingerprinting-monitor/
Submission: On December 01 via api from US — Scanned from DE
Effective URL: https://fpmon.github.io/fingerprinting-monitor/
Submission: On December 01 via api from US — Scanned from DE
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FPMON A real-time fingerprinting monitor for Chrome. Check how, where and who is analyzing you with JS fingerprinting. View the Project on GitHub fpmon/fingerprinting-monitor * Download FPMON * Download Paper * Watch Talk A FINGERPRINTING MONITOR FOR CHROME. FPMON is a browser extension that shows you where, when and which browser fingerprinting method is applied against you. You can use it to test your favorite websites and check your own websites for 3rd-party fingerprinting scripts. It can also be used to test many privacy tools. OUR TALK FROM THE CHAOS COMMUNICATION CONGRESS 2020. LONG STORY SHORT ... Browser fingerprinting has started to gain heavy tracktion, and current privacy tools do not work like expected. This is the result of our large scale study with FPMON that was done in early 2020. Some of our key findings are: * Fingerprinting is present on many websites with sensitive contents (health insurance, finances, news, NGOs, privacy tools, etc.); In fact, 20% of the 10k most popular websites apply fingerprinting. * Fingerprinting is privacy-invasive (allow user identification in many cases) and subverts current regulations like GDPR and CCPA; * Anti-tracking tools like Mozilla’s Enhanced Tracking Protection, EFF’s Privacy Badger, DuckDuckGo’s Privacy Essentials can not sufficiently protect users and convey a false sense of privacy. * Serveral large fingerprinting networks push the adoption of this technology and collect vast amounts of user data from their clients websites. Based on this data, these networks can identify users and hence track them across the web. Hence, we decided to publish FPMON as a free browser extension to empower web users against this growing threat. WANT TO READ THE FULL STORY? Read our paper that is peer reviewed at the moment. In addition, here is another paper using machine learning that strongly supports our results on the 10k most popular websites. CREATED BY Julian Fietkau Kashyap Thimmaraju Felix Kybranz Sebastian Neef Jean-Pierre Seifert Get in contact with us: dALXi2jfbmAj4KGo@systemli.org