Strength in Numbers: Robust Tamper Detection in Crowd Computations

By Bimal Viswanath, Muhammad Ahmad Bashir, Muhammad Bilal Zafar, S. Bouget, Saikat Guha, Krishna P. Gummadi, Aniket Kate, Alan Mislove. 2015

46 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Signal Processing

Citations in the corpus

Mitigating Sybils in Federated Learning Poisoning (2018, background): “These approaches use auxiliary interaction information such as an underlying social network [33], or involve detecting and rejecting malicious behavior [34].”
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning (2018, background): “Additional security work in federated learning has required that public key infrastructure exists which validates the identity of users [12], but prior sybil work has shown that relying on public key infrastructure for user validation is insufficient [52, 53].”
REV2: Fraudulent User Prediction in Rating Platforms (2018, background): “For instance, rapid or regular rating behavior is typical of fraudulent entities like fake accounts, sybils and bots [7, 16, 30, 31].”
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017, methodology): “Similarly to [40], the technique in [13] analyses a group of accounts, for detecting possible spambots among them.”
Automated Crowdturfing Attacks and Defenses in Online Review Systems (2017, background): “, by increasing the number of friends/contacts on the site) and in assisting defenses [10, 19, 20, 31, 47, 71, 75] (e.”
Characterizing and Detecting Hateful Users on Twitter (2018, methodology): “This resonates with existing methods for detecting fake accounts in which using the account’s creation date have been successful (Viswanath et al. 2015).”
The Limitations of Federated Learning in Sybil Settings (2020, background): “, information from social networks or peer-to-peer systems are not applicable [22, 53, 55, 63, 64].”