Filter List Generation for Underserved Regions
Paper i proceeding, 2020

Filter lists play a large and growing role in protecting and assisting web users. The vast majority of popular filter lists are crowd-sourced, where a large number of people manually label resources related to undesirable web resources (e.g. ads, trackers, paywall libraries), so that they can be blocked by browsers and extensions. Because only a small percentage of web users participate in the generation of filter lists, a crowd-sourcing strategy works well for blocking either uncommon resources that appear on "popular" websites, or resources that appear on a large number of "unpopular" websites. A crowd-sourcing strategy will perform poorly for parts of the web with small "crowds", such as regions of the web serving languages with (relatively) few speakers. This work addresses this problem through the combination of two novel techniques: (i) deep browser instrumentation that allows for the accurate generation of request chains, in a way that is robust in situations that confuse existing measurement techniques, and (ii) an ad classifier that uniquely combines perceptual and page-context features to remain accurate across multiple languages. We apply our unique two-step filter list generation pipeline to three regions of the web that currently have poorly maintained filter lists: Sri Lanka, Hungary, and Albania. We generate new filter lists that complement existing filter lists. Our complementary lists block an additional 3,349 of ad and ad-related resources (1,771 unique) when applied to 6,475 pages targeting these three regions. We hope that this work can be part of an increased effort at ensuring that the security, privacy, and performance benefits of web resource blocking can be shared with all users, and not only those in dominant linguistic or economic regions.

ad blocking

filter lists

crowdsource

Författare

Alexander Sjösten

Chalmers, Data- och informationsteknik, Informationssäkerhet

Peter Snyder

Brave Software Inc.

Antonio Pastor

Universidad Carlos III de Madrid

Panagiotis Papadopoulos

Brave Software Inc.

Benjamin Livshits

Imperial College London

The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020

1682-1692

29th International World Wide Web Conference, WWW 2020
Taipei, Taiwan,

Ämneskategorier

Medieteknik

Annan data- och informationsvetenskap

Datavetenskap (datalogi)

DOI

10.1145/3366423.3380239

Mer information

Senast uppdaterat

2020-08-04