Adremover: The Improved Machine Learning Approach for Blocking Ads

Vo, Thu; Jaiswal, Chetan

doi:10.1109/uemcon47517.2019.8993052

Cited by 2 publications

(1 citation statement)

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“…Moreover, Thu and Chetan proposed in [31] a new model called AdRemover based on Random Forest classification, blacklists, and whitelists. The decision trees were trained by determining which URLs are likely to contain ads or non-ads to create the filter lists automatically.…”

Section: Safae Et Al Inmentioning

confidence: 99%

Web Tracking Domain and Possible Privacy Defending Tools: A燣iterature燫eview

Bubukayr¹,

Frikha²

2022

Journal of Cyber Security

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Personal data are strongly linked to web browsing history. By visiting a certain website, a user can share her favorite items, location, employment status, financial information, preferences, gender, medical status, news, etc. Therefore, web tracking is considered as one of the most significant internet privacy threats that can have a serious impact on end-users. Usually, it is used by most websites to track visitors through the internet in order to enhance their services and improve search customization. Moreover, selling users' data to the advertising companies without their permission. Although there are more research efforts focused on third-party tracking to protect user privacy, there are still no comprehensive approaches to develop an efficient and accessible privacy protection method, even if more attention is paid to the topic. The main goal of this paper is to conduct a literature review on the web-tracking domain and possible privacy defending methods by presenting an overview of privacy issues, determining the possible tracking mechanisms that might be exploited, discussing the available privacy defense tools that could be utilized for improvement, and presenting the strength and weaknesses of each method.

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Section: Safae Et Al Inmentioning

confidence: 99%