Abstract

In an effort to legally prosecute P2P users, the RIAA and MPAA have reportedly started to create decoy users: they participate in P2P networks in order to identify illegal sharing of content. This has reportedly scared some users who are afraid of being caught and prosecuted. The question we would like to answer is how prevalent is this phenomenon: how likely is it that a user will run into such a “fake user” and thus run the risk of a lawsuit? The first challenge is identifying these “fake users”. We collect this information from a number of free open-source software projects which are trying to identify such addresses by forming the, so-called, blocklists. The second challenge is to quantify the probability of a user contacting such a fake user by conducting a large scale experiment in order to obtain reliable statistics. Using PlanetLab, we conduct active measurements, spanning a period of 90 days, from January to March 2006, spread over three continents. Analyzing over 100 GB of TCP header data, we quantify the probability of a P2P user contacting fake users. We observe that 100% of our peers run into entities in these lists. In fact, 12–17% of all distinct IPs contacted by any node were listed on blocklists. Interestingly, a little caution can have significant effect: the top five most prevalent blocklisted IP ranges contribute to nearly 94% of all blocklisted IPs we ran into. Avoiding these can reduce the probability of a user being tracked to about 1%. In addition, we examine the identity of these blocklisted IPs. The majority of blocklisted IPs belong to the commercial and government domains and are nearly 2.5 times more than IPs belonging to educational, spyware or adware entities. Interestingly, less than 0.5% of all unique IPs contacted, belong explicitly to media companies. However, this may not be reassuring for P2P users, since the other blocklist users (government or commercial) could be collaborating with media companies.

Keywords: Peer-to-peer; Blocklist; RIAA; Monitoring

Article Outline

1. Introduction

2. Relevant literature

3. Who is watching?

4. Probability of being tracked

5. Geographical distribution

6. Effect of role on the probability of being tracked

7. Conclusion

References

Vitae