Ad networks examples






Code-Level Dark Patterns: Exploring Ad Networks' Misleading Code

We present three code samples from Google and Amazon ad networks where the code samples do not provide a “I do not consent” option for location data collection 
tahaei darkpatterns


ARTICLE 29 DATA PROTECTION WORKING PARTY Opinion 2

22 juin 2010 The Opinion comments on how advertising network providers ... example by reserving different places on the website for different advertising ...
wp en


Doubleclick Ad Exchange Auction arXiv:1204.0535v1 [cs.GT] 2 Apr

2 avr. 2012 between the exchange and publishers or ad networks etc. For example what commodities should be traded? One can imagine trading contracts ...


``Developers Are Responsible'': What Ad Networks Tell Developers

We found that information is focused on complying with legal regulations and puts the responsibility for such decisions on the developer. Also
tahaei AdNetworkLBW





Charting App Developers' Journey Through Privacy Regulation

Abstract: Mobile apps enable ad networks to collect and track users. App developers are given privacy tasks for example
pets charting regulations adnetworks


Code-Level Dark Patterns: Exploring Ad Networks' Misleading Code

Code-Level Dark Patterns: Exploring Ad Networks' Misleading. Code Samples with Negative Consequences for Users. Mohammad Tahaei mohammad.tahaei@ed.ac.uk.
chi dark patterns workshop


Charting App Developers' Journey Through Privacy Regulation

ad networks present privacy regulations and how usable the provided configurations are for privacy tasks for example
popets


``Developers Are Responsible'': What Ad Networks Tell Developers

We found that information is focused on complying with legal regulations and puts the responsibility for such decisions on the developer. Also
tahaei AdNetworkLBW





Charting App Developers' Journey Through Privacy Regulation

Abstract: Mobile apps enable ad networks to collect and track users. App developers are given privacy tasks for example
popets


Perceptual Ad Blocking meets Adversarial Machine Learning

inputs (e.g. an ad-network cannot “sample” new ads until it finds one that evades the ad-blocker)


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