@ -82,7 +82,7 @@ If you additionally consider their appearance, you may be able to roughly estima
Furthermore, if you notice that the person walks into the office of Company X using an electronic badge, the anonymity set shrinks to the number of Company X's employees and visitors.
Finally, you may notice the license number of the car they used to arrive at the place.
If you are a casual observer, this doesn't give you much.
However, if you are a city official and have access to the database that matches license plate numbers with names, you can narrow down the anonymity set to just a few people: the car owner and their closest friends and relatives.
However, if you are a city official and have access to the database that matches license plate numbers with names, you can narrow down the anonymity set to just a few people: the car owner and any close friends and relatives that may have borrowed the car.
This example illustrates a few important points.
First, every bit of information may bring the adversary closer to their goal.
@ -427,7 +427,7 @@ We are particularly interested in understanding the LN channel graph, its robust
If this was the case, then the Lightning Network's degree distribution would follow a Gaussian normal distribution.
In particular, most of the nodes would have approximately the same degree, and we would not expect nodes with extraordinarily large degrees.
This is because the normal distribution exponentially decreases for values outside of the interval around the average value of the distribution.
The depiction of a random graph looks like a mesh network topology.
The depiction of a random graph (as we saw in <<lngraph>>) looks like a mesh network topology.
It looks decentralized and nonhierarchical: every node seems to have equal importance.
Additionally, random graphs have a large diameter.
In particular, routing in such graphs is challenging because the shortest path between any two nodes is moderately long.
kristen@oreilly.com
3 years ago