Award Abstract # 1719147
SaTC: CORE: Small: Collaborative: The Web Ad Technology Arms Race: Measurement, Analysis, and Countermeasures

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: REGENTS OF THE UNIVERSITY OF CALIFORNIA AT RIVERSIDE
Initial Amendment Date: August 16, 2017
Latest Amendment Date: August 16, 2017
Award Number: 1719147
Award Instrument: Standard Grant
Program Manager: Wei-Shinn Ku
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2017
End Date: August 31, 2021 (Estimated)
Total Intended Award Amount: $250,000.00
Total Awarded Amount to Date: $250,000.00
Funds Obligated to Date: FY 2017 = $250,000.00
History of Investigator:
  • Zhiyun Qian (Principal Investigator)
    zhiyun.qian@ucr.edu
Recipient Sponsored Research Office: University of California-Riverside
200 UNIVERSTY OFC BUILDING
RIVERSIDE
CA  US  92521-0001
(951)827-5535
Sponsor Congressional District: 39
Primary Place of Performance: University of California-Riverside
CA  US  92521-0001
Primary Place of Performance
Congressional District:
39
Unique Entity Identifier (UEI): MR5QC5FCAVH5
Parent UEI:
NSF Program(s): Secure &Trustworthy Cyberspace
Primary Program Source: 01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 025Z, 7434, 7923
Program Element Code(s): 806000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Online advertising plays a critical role in allowing a vast majority of web content to be offered free of charge to users, with the implicit quid pro quo agreement that users agree to watch targeted ads to support these "free" services. Unfortunately, the economic magnetism of online advertising has made it an attractive target for various types of abuses. For instance, online advertising incentivizes the widespread tracking of users across websites raising privacy and surveillance concerns. Malvertising is another serious security threat to users. As a result, ad-blockers are gaining popularity because they not only provide a clean browsing experience but also protect user security and privacy.

The research is motivated by the observation that websites are now starting an arms race to fight against ad-blockers that cause significant revenue loss to the publishers. Publishers use anti ad-blockers to detect the presence of ad-blockers and react in certain ways (e.g., reminding users to turn off ad-blockers). Specifically, the research will be primarily focused on two fronts: (1) Measuring the arms race between ad-blockers and anti ad-blockers, e.g., developing techniques to detect anti ad-blockers. (2) Understanding the technological means of anti ad-blockers and possible countermeasures that may follow on the ad-blocker side. The proposed research will inform industry stakeholders and policymakers.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Iqbal, Umar and Shafiq, Zubair and Qian, Zhiyun "The ad wars: retrospective measurement and analysis of anti-adblock filter lists" Internet Measurement Conference (IMC) , 2017 10.1145/3131365.3131387 Citation Details
Mughees, Muhammad Haris and Qian, Zhiyun and Shafiq, Zubair "Detecting Anti Ad-blockers in the Wild" Proceedings on Privacy Enhancing Technologies , v.2017 , 2017 10.1515/popets-2017-0032 Citation Details
Umar Iqbal, Peter Snyder "AdGraph: A Graph-Based Approach to Ad and Tracker Blocking" IEEE Security and Privacy , 2020 Citation Details
Zhu, Shitong and Hu, Xunchao and Qian, Zhiyun and Shafiq, Zubair and Yin, Heng "Measuring and Disrupting Anti-Adblockers Using Differential Execution Analysis" The Network and Distributed System Security Symposium (NDSS) , 2018 10.14722/ndss.2018.23331 Citation Details
Zhu, Shitong and Iqbal, Umar and Wang, Zhongjie and Qian, Zhiyun and Shafiq, Zubair and Chen, Weiteng "ShadowBlock: A Lightweight and Stealthy Adblocking Browser" WWW '19 The World Wide Web Conference , 2019 10.1145/3308558.3313558 Citation Details
Zhu, Shitong and Wang, Zhongjie and Chen, Xun and Li, Shasha and Man, Keyu and Iqbal, Umar and Qian, Zhiyun and Chan, Kevin S. and Krishnamurthy, Srikanth V. and Shafiq, Zubair and Hao, Yu and Li, Guoren and Zhang, Zheng and Zou, Xiaochen "Eluding ML-based Adblockers With Actionable Adversarial Examples" ACSAC: Annual Computer Security Applications Conference , 2021 https://doi.org/10.1145/3485832.3488008 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

PI Qian and PI Shafiq have completed the project of "The Web Ad Technology Arms Race: Measurement, Analysis, and Countermeasures". During this period, the team has initiated multiple research projects that cover various aspects of the web advertising ecosystem. As the ecosystem is largely self-regulating, online advertising has important implications for security and privacy. With the popularity of adblocking, websites have started to employ anti-adblocking which works by detecting the presence of adblockers and taking subsequent actions such as denying access. The PIs started off the project by understanding the arms race between adblocking and anti-adblocking. Through real-world measurements, they have witnessed a substantial growth of anti-blocking tactics over the years employed by popular websites.

Next, the team has also developed novel adblocking technologies in an attempt to level the plainfield. They aim at being more robust to anti-adblocking, and more automated in recognizing advertisements in a webpage. Finally, they also evaluated possibilities to subvert the adblocking technology they built themselves.

Overall, PI Qian and PI Shafiq have engaged in significant outreach of this line of research to bring awareness of online advertising and its security and privacy implications. This includes active participation in various events beyond academic conferences, e.g., FTC PrivacyCon and Data Transparency Lab (DTL) conferences.


 


Last Modified: 01/28/2022
Modified by: Zhiyun Qian

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