Financial Cryptography and Data Security: 12th International by N. Boris Margolin, Brian Neil Levine (auth.), Gene Tsudik

By N. Boris Margolin, Brian Neil Levine (auth.), Gene Tsudik (eds.)

This publication constitutes the completely refereed post-conference complaints of the twelfth overseas convention on monetary Cryptography and knowledge defense, FC 2008, held in Cozumel, Mexico, in January 2008.

The sixteen revised complete papers and nine revised brief papers provided including five poster papers, 2 panel reviews, and 1 invited lecture have been conscientiously reviewed and chosen from 86 submissions. The papers are geared up in topical sections on assaults and counter measures, protocols, idea, undefined, chips and tags, signatures and encryption, in addition to anonymity and e-cash.

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Fig. 4(b) shows a “false negative” run — latency remains quite high during the off periods. This may well be due to the fact that we selected the burst server load by measuring the average (a) (b) Fig. 5. ROC plots for MD correlation of (a) middleman and (b) victim relays Don’t Clog the Queue! 37 throughput capacity of morphmix instances on our planetlab nodes. Fig. g. the probe latencies of a disjoint node that show high correlation to the burst signal. It seems clear that the high correlation here is due to an unrelated spike in the load of the “disjoint” planetlab node.

The attacker’s strong incentive to protect herself even when it causes harm to others is a novel property of PhishTank’s voting system. The undermining attacker does not bother with such subtleties. Instead, this attacker seeks to harm the credibility of PhishTank, which is best achieved by combining attacks 1 and 2: submitting URLs for legitimate websites and promptly voting them to be phish. This attacker may also increase the confusion by attempting to create false negatives, voting phishing websites as legitimate.

The right-hand graph in Figure 3 groups user submissions together logarithmically, then plots the proportion of all invalid user submissions each group contributes. For instance, users submitting once contribute 17% of all invalid submissions. Users with fewer than 100 submissions collectively make 60% of the mistakes, despite submitting less than 7% of the phishing candidate URLs. 3 Do Users with Bad Voting Records Vote Together? We now consider whether bad decisions reinforce themselves. More precisely, we ask whether users with bad voting records are likely to vote on the same phishing reports more often than randomly.

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