Like every other privacy network, Freenet is a target of statistical attacks to trace the activity of its users.
Studies that investigated tracing Freenet users were built on unrealistic idealized setups or simplistic routing, so that their results don’t apply to the real network.
Despite these shortcomings in the studies, there have been cases of seized equipment. To prevent future cases from targeting innocents based on these misleading statistics, we want to provide an example of a clean calculation of the probability that some observation is a false positive.
A short definition: False positives are results which look like a hit, e.g. finding the originator of a request, but which are wrong, e.g. pointing to the wrong persion.
Second definition: A Freenet node is Freenet running on a computer.
When observing Freenet, false positives most likely happen because of misunderstanding how Freenet routing works, how file transfer works, or how connections in Freenet are structured in real operation.
In the article tracking efforts based on false statistics, we already showed how false results occur due to specific misunderstandings about the concepts used in Freenet routing. The current article shows how false positives happen due to using a false idea about the actual structure of the Freenet network.
Firstoff: In an idealized structure, each node has 6 connections, all nodes provide the same bandwidth, and all connections are usable all the time. Such an idealized lattice of nodes looks like the following:
6 6
6 6 6
6 6
6 6 6
6 6
In the real network at the time of writing, the number of connections varies between 5 and 65, depending on the bandwidth available at the nodes. A snapshot of the connectioncount distribution can be seen on the Freenet statistics site. Between 10% and 80% of the connections are inactive due to overload (backoff). This increased when groups of users started to patch their nodes to request data at a higher rate than the rest.
A more realistic structure therefore looks like this:
80 6 6
70 6
65 6
60 6
55 13
50 24 13
42 13
39 15
36 18
33 21
30 28 24
6
70 13
55 7 20
40 30
The difference to the idealized structure which is most important to this article is that almost every node has at least one connection to another node with 8 connections or less. Also several of these connections are in backoff, so they are not actually used, which easily reduces the effective connection count to 4. From now on I will call such nodes “small nodes”.
If the connection count of a node is just 4, the requests a neighboring node forwards from a single download look very similar to requests from the neighboring node if it has 4 downloads running.
Therefore whenever you see requests which could originate from a given node, you must check how likely it is that they were actually sent from such a small node.
The first step is to check whether we can exclude a small node as likely originator. Freenet assigns the number of connections based on the assigned bandwidth. A node with 4x the bandwidth has 2x the connections. Therefore, if its user did not actively change its code, a small node has low bandwidth.
As a simple test, I downloaded a file with roughly 20 MiB on a node with 8 connections as maximum, 6 active connections on average. It downloaded 2 MiB per minute. Scaling up, a node with 16 connections should download about 8 MiB per minute. If you observe a download of 400 MiB that takes 2 hours or more, it is possible that it comes from a small node with around 11 peers (89 working at any time).
If you see 400MiB take only 1 hour or you see 1 GiB downloaded in 2 hours, it is more likely that the originator has 16 connections or more. With some tricks that can be increased, so as a rule of thumb to exclude a small node you would have to observe a download of 400 MiB taking only half an hour. Due to asymmetric connections a small node is typically one with slow upload, not with slow download.
With these basics in place, we’ll show the rest with a scenario.
Assume that there is a monitorying node that observed requests coming from a node with 50 peers. The file in question is 400 MiB big and the download lasted slightly more than two hours. Assume that you observed requests for 4% of the file from the 50 peer node.
In an idealized uniform network without friendofafriend (FOAF) routing, you would now assume that you are connected to the originator. But due diligence requires that you correct for FOAF routing and the real nonuniform structure, and check for false positives.
How likely is it that the requests were started by one of the roughly 8 small nodes connected to a typical 50peernode?
A typical false positive would be that within those two hours, a node you were connected to tried to retrieve the file. Let’s only use information we actually received (without naming names). We’ll clearly mark where we have to take assumptions, and where this is due to lacking required information.
Let’s start with the information:
between 3:50 PM UTC and 6:08 PM UTC the Freenet node requested 383 unique blocks. The Freenet node reported an average of 51.3 peers. To reconstruct the file requires a minimum of 12,723 blocks of a total possible of 24,874.
 minimum required blocks: 12,723
 the node had 50 peers
 the observer saw 383 blockrequests sent via the connection with you
The node had 50 peers.
At that time about 25% of nodes had less than 10 peers (peek at 7 peers), 15% of nodes had only 10 to 15 peers, with the rest evenly spread between 16 and 70. Only about 20% of nodes have 50 peers or more. See the ^{1} footnote at the end for the origin of this data.
Assumptions: the node was connected to a node with 7 peers, and that node requested the file. From the peers of that node, you were the only with 50 peers or more. Then there was a node with 30 peers. Then two nodes with 15 peers each, and three nodes with 7 peers each.
 assumption: actual originator had 7 peers
 assumption: the peercount distribution was typical
This is still a typical situation (not a rare one).
Originatorconnections:
 node: 50 peers.
 A: 30 peers.
 B: 15 peers.
 C: 15 peers.
 D: 7 peers.
 E: 7 peers.
 F: 7 peers.
We do not know the number of peers of the observer node, so let’s assume that it has many connections to see a larger share of the traffic. Let’s assume 70, because that’s what I would do.
 assumption: observer had 70 peers. (information lacking)
First step: The originator requests only the minimum required blocks of the file, because all requests succeed. In absolute numbers: 12,723 requests.
These are distributed over the peers. In a typical situation, about one in three peers is backed off. Let’s assume the routable hosts during the request to be the node, A, C, D and E. B and F are backed off.
Routable:
 node: 50 peers.
 A: 30 peers.
 C: 15 peers.
 D: 7 peers.
 E: 7 peers.
Now those requests are distributed via FOAFRouting not evenly but by peercount. There are in total 119 second degree peers, so the node will receive on average 50/119 * 12723 requests, which would be 5345 requests.
Now we get to the node. Let’s assume a typical distribution again. Since it has many peers, it will stick closer to the statistical nodecount due to stronger sampling. It will have 10 nodes with 50 or more peers, one of which is the observer node. As usual, 30% will be backed off.
The routable connections (not backed off):
 1 Observer: 70 peers.
 6 with 60 peers.
 13 with 30 peers.
 5 with 15 peers.
 10 with 7 peers.
The backed off connections:
 (3 with 60 but backed off).
 (7 with 30 but backed off).
 (2 with 15 but backed off).
 (3 with 7 but backed off).
This gives a total number of 965 secondlevel peers via routable connections, of which the observer watches 70. So you’d expect that the observer will see 5345 * 70 / 965 requests: Total requests you received multiplied by the peers of the observer and divided by the total count of routable secondlevel peers.
5345 * 70 / 965 = 387.720207253886.
This number of requests is therefore confirmed as a likely false positive. It occurs in a typical scenario where the node is not the requester.
The short of it: The argumentation does NOT show that the node is likely the requester of the file. Not even in a typical situation. The most likely situation is that a node this node was connected to requested the file without the nodes knowledge. If we’d use atypical but often occurring situations, this would be even clearer.
Sidenote: A calculation like this is not sufficient to show that someone is guilty. It only shows that the information provided CANNOT show guilt, because it is very likely to be a false positive.
This is for a file where all blocks succeeded. For a file that’s on the brink of dropping out, you’d expect two times as many requests. If the actual requester had more peers, you’d expect fewer requests. If the requester had only nodes with few peers, you’d expect more requests. And this is without actually looking for evidence. This is just disproving the claim using the much too limited information from the affidavit by showing that this is most likely a false positive.
Besides: Argumentation like the following argumentation is false to a seriously annoying degree:
minimum of 12,723 blocks of a total possible of 24,874. These 383 blocks represent 155% of the even, or expected, share of the minimum block (12,723) required to download the file and 79% of the even or expected
Those 3% of the file the observer saw are 155% of what you’d expect if all the nodes peers had the same number of peers. But that is a false assumption, as you can already see from the example distribution given for the originator. The number of blocks requested scale with the peercount of each peer. So if the observer node had 60 peers while a typical node had 20 peers, the observer would automatically receive 3x as many requests as with even share.
As a note: The peercount can change from release to release when parameters are optimized. The argumentation here stays the same, but the numbers change a bit. People will have to look at the peer count distribution during the time of the measurement.
Final note: The minimal information required for statistical claims about observations of node upload or download activity in Freenet:

The exact time and HTL of each watched chunk that was seen from the node
 per chunk: nodelocation of the observer at the time
 per chunk: nodelocation of the observed at the time
 per chunk: nodelocations of all peers of the observer at the time
 per chunk: nodelocations of all peers of the observed at the time
 perchunk: the manifest it belongs to (only size + index in some list + number of chunks in the manifest)
 per chunk: routing part of the key of the chunk (no decryption possible from this info => data not accessible)

The exact formula of the probability that the observed is a valid target
 The exact formula of the probability that the observed is not a false positive

The results of applying those formula to the data along with the data, so they can be checked independently.

all chunks received at HTL <= 16 which would be a match if at HTL > 16
 The peercounts they observed on that day in all nodes they connected to (a plain list of numbers)
 Keys for chunks should be truncated by cutting or blacking at least 4 letters, so they cannot easily be used to download the associated data, though the full keys must be provided on request to an independent trusted party (i.e. the defense lawyer) to verify that they contain what is claimed. Otherwise they could just make up claims from thin air.
definition: watched chunks are those which are recorded if received from the observed or sent to the observed, as well as those which would be recorded if received by the observer or sent by the observer.
If observers cannot provide this minimal information, they cannot get a robust statistical result. If they do not want to provide this to a court, they prevent the court from checking their claims.
Yes, it is hard to correctly trace activity in Freenet to a specific user. Without this property, Freenet could not protect Freedom of speech and of the press, both of which are under attack in many countries around the world.

The peercount is taken from the statistics in june and october, versions 334 and 355 as found via the datehints for that site, counted by eye: SSK@WMa1Z40iYdZZ51yctQ3toFl9zuuFEnNdsm3NejJU5KE,jCBcaNBeKD5~sSQeSkyKz737Bh5ibBGqdzfD8mgfdMY,AQACAAE/statisticsDATEHINT20189?type=text/plain SSK@WMa1Z40iYdZZ51yctQ3toFl9zuuFEnNdsm3NejJU5KE,jCBcaNBeKD5~sSQeSkyKz737Bh5ibBGqdzfD8mgfdMY,AQACAAE/statisticsDATEHINT201810?type=text/plain SSK@WMa1Z40iYdZZ51yctQ3toFl9zuuFEnNdsm3NejJU5KE,jCBcaNBeKD5~sSQeSkyKz737Bh5ibBGqdzfD8mgfdMY,AQACAAE/statistics334/plot_peer_count.png SSK@WMa1Z40iYdZZ51yctQ3toFl9zuuFEnNdsm3NejJU5KE,jCBcaNBeKD5~sSQeSkyKz737Bh5ibBGqdzfD8mgfdMY,AQACAAE/statistics355/plot_peer_count.png ↩