Examining Signals of Trust in Criminal Markets Online

• NCJ Number: 255250
• Journal: Journal of Cybersecurity Volume: 2 Issue: 2 Dated: December 2017 Pages: 137-145
• Author(s): Thomas J. Holt; Olga Smirnova; Alice Hutchings
• Date Published: December 2017
• Length: 9 pages
• Grant Number(s): 2010-IJ-CX-1676
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description
• Award: Examining the Structure, Organization, and Processes of the International Market for Stolen Data Online

Annotation

This study examined the factors associated with positive and negative buyer feedback from the purchase of stolen credit card data in a series of advertisements from a sample of Russian- and English-language forums where individuals buy and sell personal information.

Abstract

Understanding information asymmetry in stolen data markets is essential for acquiring knowledge of the signals that demonstrate a seller is trustworthy, as well as for identifying the formal and informal factors that encourage vendor success. Recognizing the practices of sellers and their influence on buyer reviews can also increase understanding of the social relationships that affect individual's position within the market generally. In turn, the findings may enable identification of the factors that encourage market failure and reduce demand by pushing quality sellers out of the market. The results of zero-inflated Poisson regression models suggest that the sellers may influence their likelihood of receiving feedback by specifying the type of payment mechanism, choosing the advertisement language and selecting the type of market they operate within. The implications of this study for understanding online illicit markets, criminological theory, and policymaking are explored. The findings of this analysis suggest that the conditions within stolen data markets may be manipulated and destabilized through the introduction of information asymmetry. It may be possible to complicate the process of interpreting signals by flooding the market with multiple false posts for products, as well as feedback for sellers. This technique, referred to as a Sybil attack, may be effective in creating too many false signals and increasing the difficulty in determining who is a reliable vendor. 3 table and 51 references (publisher abstract modified)