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182 network so that uploading a video involves sending a message via Ostra to a single ‘YouTube’ account in the network. An existing, well-connected user (1,376 links) in the core of the network was selected to represent this account. We first show that the rate at which users receive unwanted communication varies with the number of attacking users. In Figure 8.8, we present the results of experi- ments in which we vary the number of attackers in the network between 1 and 4,096 users (0.0002% to 1% of the network). We examine the rate at which unwanted mes- sages were received by non-attacking users, along with the expected bound derived from the equations in Section 8.4.4. As can be seen in Figure 8.8, Ostra effectively bounds the number of unwanted messages in proportion to the fraction of users who send unwanted communication. Even with 1% of the network sending unwanted messages, each legitimate user receives only 0.22 unwanted messages per week, translating to approximately 12 unwanted messages per year. Next, we explore Ostra’s sensitivity to system parameter settings and other con- ditions. Important parameters in Ostra are the credit bounds L and U for each link. If these bounds are set too high, attackers can send many messages before being cut off. However, if these bounds are set too low, a legitimate user could be temporarily prevented from sending messages. Figure 8.9 shows how the rate of unwanted message delivery is affected by the maximal credit imbalance across a link. As the maximum allowed imbalance increases, the amount of unwanted communication received byPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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