Strength in Numbers: Robust Tamper Detection in Crowd Computations
Abstract
Popular social and e-commerce sites increasingly rely on crowd computing to rate and rank content, users, products and businesses. Today, attackers who create fake (Sybil) identities can easily tamper with these computations. Existing defenses that largely focus on detecting individual Sybil identities have a fundamental limitation: Adaptive attackers can create hard-to-detect Sybil identities to tamper arbitrary crowd computations.
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The technique proposed by Viswanath et al. [40] is capable of spotting the differences between groups of genuine accounts and the new wave of social spambots.
“Thus, the technique proposed in [40] is capable of spotting the differences between groups of genuine accounts and the new wave of social spambots.”
From The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017) · cites this paper as [40]
The technique proposed by Viswanath et al. [40] cannot directly spot the tampering accounts, so detection and removal of single accounts must be performed using a separate methodology.
“However, the technique cannot directly spot the tampering accounts, and, thus, detection and removal of the single accounts must be performed using a separate methodology.”
From The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017) · cites this paper as [40]
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“There are thriving un-derground markets for launching such tampering attacks on the crowd-sourcing sites mentioned above [27, 32, 35]; typically, the more popular a site, the greater the frequency and magnitude of such attacks.”
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- Authors
- Viswanath, B., Bashir, M. A., Zafar, M. B., Bouget, S., Guha, S., Gummadi, K. P., Kate, A., & Mislove, A.
- Affiliations
- Max Planck Institute for Software Systems (DE)
- Northeastern University (US)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Microsoft Research (India) (IN)
- Purdue University West Lafayette (US)
- Venue
- Proceedings of the 2015 ACM on Conference on Online Social Networks
- Publisher
- ACM
- Published
- 2015-11-02
- Pages
- 113-124
- OpenAlex
- W1964235853
- Cited by
- 43
- References
- 31
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 1 · 2021: 3 · 2020: 5 · 2019: 11 · 2018: 10 · 2017: 6 · 2016: 5
- FWCI
- 6.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Conference on Online Social Networks (publishedVersion)
- Funders
- National Science Foundation (CNS-1319019, CNS-1421444)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Keywords
- Sybil attack, crowd computing, content rating, user ranking, e-commerce, social networks
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