Anti-aliasing on the web

ABSTRACT

It is increasingly common for users to interact with the web using a number of different aliases. This trend is a double-edged sword. On one hand, it is a fundamental building block in approaches to online privacy. On the other hand, there are economic and social consequences to allowing each user an arbitrary number of free aliases. Thus, there is great interest in understanding the fundamental issues in obscuring the identities behind aliases.However, most work in the area has focused on linking aliases through analysis of lower-level properties of interactions such as network routes. We show that aliases that actively post text on the web can be linked together through analysis of that text. We study a large number of users posting on bulletin boards, and develop algorithms to anti-alias those users: we can with a high degree of success identify when two aliases belong to the same individual.Our results show that such techniques are surprisingly effective, leading us to conclude that guaranteeing privacy among aliases that post actively requires mechanisms that do not yet exist.

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INDEX TERMS

Primary Classification:
  E. Data
  E.4 CODING AND INFORMATION THEORY

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
      I.2.7 Natural Language Processing
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering

General Terms:
Algorithms, Measurement, Security, Theory

Keywords:
alias detection, aliases, bulletin boards, personas, privacy, pseudonyms