In general, side information is the information other than the target signal that can help improve system performance. For instance, in digital communications, side information about channel conditions at the transmitter's side can help reduce the bit error rate, and in learning theory, the side information map can also improve the classification accuracy. In this chapter, we use multimedia fingerprinting as an example and discuss how side information affects user behavior in media-sharing social networks.

In the scalable fingerprinting system in Chapter 5, given a test copy, the fingerprint detector simply uses fingerprints extracted from all layers collectively to identify colluders, and does not use any other information in the detection process. Intuitively, if some information about collusion can be made available during the colluder identification process, using such side information can help improve the traitor-tracing performance. In this chapter, we investigate two important issues in multimedia fingerprinting social networks that are related to side information: which side information can help improve the traitor-tracing performance, and how it affects user behavior in multimedia fingerprinting systems.

In this chapter, we first examine which side information can help improve the traitor tracing performance; our analysis shows that information about the statistical means of the detection statistics can significantly improve the detection performance. We then explore possible techniques for the fingerprint detector to probe and use such side information, and analyze its performance.