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195 tination, the bound is restored. Otherwise, if a user drops the message, each of the users penalizes the next hop after the timeout T . An example is shown in Figure 8.12: while the message is pending classification (b), both the upper bound U and the lower bound L are changed to account for all possible outcomes. In the case in which Z drops the message (c), X penalizes Y , and Y penalizes Z. Thus, Z is penalized for dropping the message, whereas Y , who properly forwarded the message, has a neutral outcome. 8.5.6 Security and privacy One concern is whether malicious users can abuse the false positives in the bloom filters to attract request to be routed through them. In order to maintain an accept- able false positive rate, users select the number of hash functions and the bloom filter length so that the number of bits set to 1 is less than a specified values Bmax. Any bloom filters with more than Bmax bits set is ignored. Otherwise, malicious users could simply create bloom filters consisting of all 1s, implying that they would be a good choice when routing to any other user. Appropriate settings can be indepen- dently determined based on the size of the their two hop neighborhood. In Ostra, it is not possible to explore the trust network beyond two hops, due to the use of private aliases. By requesting the bloom filters of many nodes, it is however possible to determine the identities of friends of a friend with high probability. However, many deployed social networks, such as FaceBook [49] and LinkedIn [95]PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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