Friends or Foes: Distributed and Randomized Algorithms to Determine Dishonest Recommenders in Online Social Networks

TL;DR

This paper introduces distributed and randomized algorithms to detect dishonest recommenders in online social networks, aiming to improve security in viral marketing by identifying malicious users who give misleading recommendations.

Contribution

It proposes novel fully distributed algorithms for detecting dishonest users in OSNs and provides a performance analysis including false positive/negative probabilities.

Findings

Algorithms effectively identify dishonest recommenders.

Simulation results show high detection accuracy.

Performance metrics quantify detection reliability.

Abstract

Viral marketing is becoming important due to the popularity of online social networks (OSNs). Companies may provide incentives (e.g., via free samples of a product) to a small group of users in an OSN, and these users provide recommendations to their friends, which eventually increases the overall sales of a given product. Nevertheless, this also opens a door for "malicious behaviors": dishonest users may intentionally give misleading recommendations to their friends so as to distort the normal sales distribution. In this paper, we propose a detection framework to identify dishonest users in OSNs. In particular, we present a set of fully distributed and randomized algorithms, and also quantify the performance of the algorithms by deriving probability of false positive, probability of false negative, and the distribution of number of detection rounds. Extensive simulations are also carried out to illustrate the impact of misleading recommendations and the effectiveness of our detection algorithms. The methodology we present here will enhance the security level of viral marketing in OSNs.