The Influence of Sociological Variables on Users’ Feelings about Programmatic Advertising and the Use of Ad-Blockers

The evolution of digital advertising, which is aimed at a mass audience, to programmatic advertising, which is aimed at individual users depending on their profile, has raised concerns about the use of personal data and invasion of user privacy on the Internet. Concerned users install ad-blockers that prevent users from seeing ads and this has resulted in many companies using antiad-blockers. This study investigates the sociological variables that make users feel that advertising is annoying and then decide to use ad-blockers to avoid it. Our results provide useful information for companies to appropriately segment user profiles. To do this, data collected from Internet users (n = 19,973) about what makes online advertising annoying and why they decide to use ad-blockers are analyzed. First, the existing literature on the subject was reviewed and then the relevant sociological variables that influence users’ feelings about online advertising and the use of ad-blockers were investigated. This work contributes new information to the discussion about user privacy on the Internet. Some of the key findings suggest that Internet advertising can be very intrusive for many users and that all the variables investigated, except marital status and education, influence the users’opinions. It was also found that all the variables in this study are important when a user decides to use an ad-blocker. A clear and inverse correlation between age and opinion about advertising as annoying could be seen, along with a clear difference of opinion due to gender. The results suggest that users without children use ad-blockers the least, while retirees and housewives use them the most

Filter List Generation for Underserved Regions

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

Actions speak louder than words: Semi-supervised learning for browser fingerprinting detection

As online tracking continues to grow, existing anti-tracking and fingerprinting detection techniques that require significant manual input must be augmented. Heuristic approaches to fingerprinting detection are precise but must be carefully curated. Supervised machine learning techniques proposed for detecting tracking require manually generated label-sets. Seeking to overcome these challenges, we present a semi-supervised machine learning approach for detecting fingerprinting scripts. Our approach is based on the core insight that fingerprinting scripts have similar patterns of API access when generating their fingerprints, even though their access patterns may not match exactly. Using this insight, we group scripts by their JavaScript (JS) execution traces and apply a semi-supervised approach to detect new fingerprinting scripts. We detail our methodology and demonstrate its ability to identify the majority of scripts ($\geqslant$94.9%) identified by existing heuristic techniques. We also show that the approach expands beyond detecting known scripts by surfacing candidate scripts that are likely to include fingerprinting. Through an analysis of these candidate scripts we discovered fingerprinting scripts that were missed by heuristics and for which there are no heuristics. In particular, we identified over one hundred device-class fingerprinting scripts present on hundreds of domains. To the best of our knowledge, this is the first time device-class fingerprinting has been measured in the wild. These successes illustrate the power of a sparse vector representation and semi-supervised learning to complement and extend existing tracking detection techniques