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gtheodor

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You can get the L_k distances between the two counters. E.g. if you sum the absolute value of the difference of the counts, you get the L_1 distance between the counters. If you raise them to the n^th power and then sum them, you get the L_n distance. For n=2, that's the Euclidean distance (squared).

A nitpick: When discussing hash function properties (p. 111), his definition of property #1 ("[it should be hard to] modify a message without changing the hash") is, to me, equivalent to second-preimage resistance ("[Given] a message m, it should be difficult to find another message m' with the same hash"), which he discusses as a separate property under prop #3.

In the explanation of prop #1, he makes it clear that he is talking about the "avalanche effect," so it might be better to say that explicitly in the definition.

Disclaimer: I'm not a crypto person, so it's better if the author comments on this as well.

I think the book is great, by the way, especially the writing style and the choice of what to focus on. Thanks, lvh, for doing this.

From the article: "Even American Express Co., which historically has focused on customers who pay their bills off every month, is now concentrating on lending money to consumers who keep a balance.

Outstanding balances reached nearly $952 billion in the first quarter, ..."

This seems to imply that, by "keep a balance," they mean not paying your bill off every month.