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181 is the degree of the user. Thus, the rate at which a malicious user can send unwanted communication is in direct proportion to her degree. As the d or L parameters are increased, we expect the rate of unwanted communication to increase accordingly. Additionally, as the proportion of malicious users in the network increases, we expect the overall rate of unwanted messages to increase. Random Proximity YouTube Bound 0.1 0.01 0.001 0.0001 0.001 0.01 0.1 1 Proportion of attackers (%) Figure 8.8 : Amount of unwanted communication received by good users as the number of attackers is varied. As the number of attackers is increased, the number of unwanted messages delivered scales linearly. Preventing unwanted communication In this section we verify experimentally that Ostra performs as described in Sec- tion 8.4.4. Unless otherwise noted, the experiments were run with 512 randomly chosen attackers (approximately 0.1% of the population), L=–3, U=3, and d=10% per day. Each good user sent 2 messages and each attacker sent 500 messages. To evaluate Ostra in the context of a content-sharing site, we modeled Ostra working in conjunction with YouTube. For these experiments, we configured the Unwanted messages received (messages/user/week)PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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