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180 2 6 0.38% 0.57% Sending Receiving Average classification Fraction delay (h) delayed 2 1.3% 6 1.3% Delay (h) Avg. Med. Max. 2.2 1.9 7.6 6.1 5.3 23.6 4.1 3.2 13.2 16.6 14.7 48.6 Table 8.3 : Message delays in sending and receiving with L=–3 and U=3. The delays are shown for heavy email users (2 hour average classification delay) and casual email users (6 hour average classification delay). 8.4.4 Effectiveness of Ostra In this section, we simulate deployments of Ostra in a message-based system (such as the messaging service on Flickr) and in a content-sharing system (such as YouTube). We evaluate Ostra under three traffic workloads: Random, where users select destina- tions randomly; Proximity, where users select destinations with the distribution that was observed in Section 8.4.2; and YouTube, where users send to a single YouTube account in the network. We show that in all cases, Ostra effectively bounds the rate at which malicious users can send unwanted communication while not impeding wanted communication. Expected performance Ostra limits the amount of unwanted communication that can be sent. A single user user can send unwanted communication at a rate of at most d∗L∗D+S , where DPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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