Award Abstract # 1616234
NeTS: Small: Collaborative Research: Leveraging Personalized Internet Services to Combat Online Trolling

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: NORTHEASTERN UNIVERSITY
Initial Amendment Date: August 8, 2016
Latest Amendment Date: August 8, 2016
Award Number: 1616234
Award Instrument: Standard Grant
Program Manager: Darleen Fisher
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2016
End Date: September 30, 2020 (Estimated)
Total Intended Award Amount: $249,337.00
Total Awarded Amount to Date: $249,337.00
Funds Obligated to Date: FY 2016 = $249,337.00
History of Investigator:
  • Alan Mislove (Principal Investigator)
    amislove@ccs.neu.edu
Recipient Sponsored Research Office: Northeastern University
360 HUNTINGTON AVE
BOSTON
MA  US  02115-5005
(617)373-5600
Sponsor Congressional District: 07
Primary Place of Performance: Northeastern University
360 HUNTINGTON AVE
BOSTON
MA  US  02115-5005
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): HLTMVS2JZBS6
Parent UEI:
NSF Program(s): Networking Technology and Syst,
Secure &Trustworthy Cyberspace
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7434, 7923
Program Element Code(s): 736300, 806000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Today, almost every browsing click that users make is collected by numerous trackers associated with a variety of online services (e.g., advertising networks, online social networks, e-commerce platforms). Users have often expressed concern about the lack of privacy and control over their personal data. Nonetheless, despite a substantial effort to expose and control this prevalent behavior, the reality is that users keep accepting updated online privacy policies, which in turn grant the gathering of more personal data. This project explores re-using this extensive tracking infrastructure for the benefits of both the users themselves and web services, with a goal of preventing online trolling (scenarios in which various groups deploy tactics to influence public opinion on the Internet, by leaving biased, false, misleading, and inauthentic comments, and then artificially amplifying their ratings). The project aims to show how the tracking infrastructure can be re-used as a user "fingerprint", allowing a lightweight and privacy-preserving form of identification for third-party web sites.

Intellectual Merit: In more detail, the project explores whether it is possible to utilize the ubiquitous online tracking of users for the direct benefit of the users themselves. Despite the fact that almost every browser click made over the last decade has been monitored by numerous online trackers, users often have a hard time proving their identity and uniqueness while using the Internet. On the other hand, many systems that rely upon open membership are often targets of online trolling, often via multiple-identity (Sybil) attacks. Organized trolling has become a serious problem in today?s Internet; some argue that it can have a profound impact on the society. The project is developing a system that would take direct advantage of the work online trackers do to record and interpret users' behavior. The key idea is to use the readily-available personalized content---generated by online trackers in real-time---as a means to verify an online user's uniqueness in a seamless and privacy-preserving manner. This personalized content, which would be collected by the users, stripped of identifying content, and uploaded, would be used to construct a multi-tracker vector representation of the user. The vector representation would then serve as a unique "fingerprint" of each user, making it difficult for attackers to appear as if they were many distinct users, thereby mitigating many trolling attacks.

Broader Impacts: The project has the capacity to make a significant impact by empowering the Internet community to combat the online trolling problem. The system under development will not only help numerous online communities regain trolling-free environments, but also re-use the extensive tracking infrastructure that already exists to provide tangible benefits for both end users and web sites. The PIs plan to design and disseminate personalization-based counter-trolling system as open-source software, which will enable effective trolling detection and counter-trolling methods and systems. Education is an integral part of the project, and the PIs plan to leverage existing institutional and special programs to recruit students from underrepresented groups into the project.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Athanasios Andreou, Giridhari Venkatadri, Oana Goga, Krishna P. Gummadi, Patrick Loiseau, and Alan Mislove "Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations" Proceedings of the Network and Distributed System Security Symposium (NDSS'18) , 2018
Athanasios Andreou, Márcio Silva, Fabrício Benevenuto, Oana Goga, Patrick Loiseau, and Alan Mislove "Measuring the Facebook Advertising Ecosystem" In Proceedings of the Network and Distributed System Security Symposium (NDSS'19), , 2019
Avijit Ghosh, Giridhari Venkatadri, and Alan Mislove "Analyzing Facebook Political Advertisers' Targeting." In Proceedings of the Workshop on Technology and Consumer Protection (ConPro'19) , 2019
Giridhari Venkatadri, Alan Mislove, and Krishna P. Gummadi "Treads: Transparency-Enhancing Ads." In Proceedings of the Workshop on Hot Topics in Networks (HotNets'18) , 2018
Giridhari Venkatadri, Elena Lucherini, Piotr Sapiezy?ski, and Alan Mislove "Investigating sources of PII used in Facebook's targeted advertising" In Proceedings of the Privacy Enhancing Technologies Symposium (PETS'19) , 2019
Giridhari Venkatadri, Piotr Sapiezy?ski, Elissa M. Redmiles, Alan Mislove, Oana Goga, Michelle Mazurek, and Krishna P. Gummadi "Auditing Offline Data Brokers via Facebook's Advertising Platform." In Proceedings of the International World Wide Web Conference (WWW'19) , 2019
Giridhari Venkatadri, Yabing Liu, Athanasios Andreou, Oana Goga, Patrick Loiseau, Alan Mislove, and Krishna P. Gummadi "Privacy Risks with Facebook's PII-based Targeting: Auditing a Data Broker's Advertising Interface" Proceedings of the IEEE Symposium on Security and Privacy (IEEE S&P'18) , 2018
Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke "Discrimination through optimization: How Facebooks ad delivery can lead to biased outcomes" In Proceedings of the ACM conference on Computer Supported Cooperative Work (CSCW'19) , 2019 10.1145/3359301
Till Speicher, Muhammad Ali, Giridhari Venkatadri, Filipe Nunes Ribeiro, George Arvanitakis, Fabricio Benevenuto, Krishna P. Gummadi, Patrick Loiseau, and Alan Mislove "On the Potential for Discrimination in Online Targeted Advertising" Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*'18) , 2018

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.

Broader Impact:

Public opinion is of paramount importance in any society. It is thus not a surprise that many governments, political parties, and various other groups deploy tactics to influence public opinion on the Internet, a practice commonly referred to as trolling. Organized trolling has already become a serious problem, and some argue that it can have a profound impact on the society. While resolving versions of this problem in the past has been done in the context of individual systems, e.g., a social network, resolving the problem comprehensively at the Internet-scale is of paramount importance for the future of the Internet. The key contribution of this project lies in developing novel methods for utilizing online trackers to counter Sybil attacks, which are often used as a vehicle behind online trolling, and in developing new methods in mitigating discrimination in online targeted advertising. 

Intellectual Merit:

Co-Opt: User tracking has become ubiquitous practice on the Web, allowing services to recommend behaviorally-targeted content to users. We design Co-Opt, a system that utilizes such readily available personalized content, generated by recommendation engines in real time, as a means to tame Sybil attacks. In particular, by using ads and other tracker-generated recommendations as implicit user “certificates”, Co-Opt is capable of creating meta-profiles which allow for rapid and inexpensive validation of users’ uniqueness, thereby enabling an Internet-wide Sybil defense service. We demonstrate the feasibility of such a system, exploring the aggregate behavior of recommendation engines on the Web and demonstrating the richness of the meta-profile space defined by such inputs. We further explore the fundamental properties of such meta-profiles, i.e., their construction, uniqueness, persistence, and resilience to attacks. By conducting a user study, we show that the user meta-profiles are robust and show important scaling effects. We demonstrate that utilizing even a moderate number of popular Web sites empowers Co-Opt to tame large-scale Sybil attacks. 

 

Mitigating discrimination in online advertising: Our work has shed significant light on the targeted advertising services that serve as the economic underpinning of many popular web services.  These advertising services allow advertisers to target specific users, rather than specific behaviors (e.g., search terms, web visits, etc).  While these systems bring significant benefits to advertisers, we have shown that they open up new risks for users. Additionally, our work on measuring ad delivery skew demonstrated previously unknown mechanisms that can lead to potentially discriminatory ad delivery, even when advertisers set their targeting parameters to be highly inclusive. This underscores the need for policymakers and platforms to carefully consider the role of the ad delivery optimization run by ad platforms themselves—and not just the targeting choices of advertisers—in preventing discrimination in digital advertising. Our work on studying the provenance of users’ personally identifiable information data on Facebook’s data gathered significant press coverage and it was cited in the independent Facebook Civil Rights Audit.


Last Modified: 12/18/2020
Modified by: Alan Mislove

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