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GNUnet's NSE subsystem

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NSE stands for Network Size Estimation. The NSE subsystem provides other subsystems and users with a rough estimate of the number of peers currently participating in the GNUnet overlay. The computed value is not a precise number as producing a precise number in a decentralized, efficient and secure way is impossible. While NSE's estimate is inherently imprecise, NSE also gives the expected range. For a peer that has been running in a stable network for a while, the real network size will typically (99.7% of the time) be in the range of [2/3 estimate, 3/2 estimate]. We will now give an overview of the algorithm used to calcualte the estimate; all of the details can be found in this technical report.

Motivation

Some subsytems, like DHT, need to know the size of the GNUnet network to optimize some parameters of their own protocol. The decentralized nature of GNUnet makes efficient and securely counting the exact number of peers infeasable. Although there are several decentralized algorithms to count the number of peers in a system, so far there is none to do so securely. Other protocols may allow any malicious peer to manipulate the final result or to take advantage of the system to perform DoS (Denial of Service) attacks against the network. GNUnet's NSE protocol avoids these drawbacks.

Security

The NSE subsystem is designed to be resilient against these attacks. It uses proofs of work to prevent one peer from impersonating a large number of participants, which would otherwise allow an adversary to artifically inflate the estimate. The DoS protection comes from the time-based nature of the protocol: the estimates are calculated periodically and out-of-time traffic is either ignored or stored for later retransmission by benign peers. In particular, peers cannot trigger global network communication at will.

Principle

The algorithm calculates the estimate by finding the globally closest peer ID to a random, time-based value.

The idea is that the closer the ID is to the random value, the more "densely packed" the ID space is, and therefore, more peers are in the network.

Example

Suppose all peers have IDs between 0 and 100 (our ID space), and the random value is 42. If the closest peer has the ID 70 we can imagine that the average "distance" between peers is around 30 and therefore the are around 3 peers in the whole ID space. On the other hand, if the closest peer has the ID 44, we can imagine that the space is rather packed with peers, maybe as much as 50 of them. Naturally, we could have been rather unlucky, and there is only one peer and happens to have the ID 44. Thus, the current estimate is calculated as the average over multiple rounds, and not just a single sample.

Algorithm

Given that example, one can imagine that the job of the subsystem is to efficiently communicate the ID of the closest peer to the target value to all the other peers, who will calculate the estimate from it.

Target value

The target value itself is generated by hashing the current time, rounded down to an agreed value. If the rounding amount is 1h (default) and the time is 12:34:56, the time to hash would be 12:00:00. The process is repeated each rouning amount (in this example would be every hour). Every repetition is called a round.

Timing

The NSE subsystem has some timing control to avoid everybody broadcasting its ID all at one. Once each peer has the target random value, it compares its own ID to the target and calculates the hypothetical size of the network if that peer were to be the closest. Then it compares the hypothetical size with the estimate from the previous rounds. For each value there is an assiciated point in the period, let's call it "broadcast time". If its own hypothetical estimate is the same as the previous global estimate, its "broadcast time" will be in the middle of the round. If its bigger it will be earlier and if its smaler (the most likely case) it will be later. This ensures that the peers closests to the target value start broadcasting their ID the first.

Controlled Flooding

When a peer receives a value, first it verifies that it is closer than the closest value it had so far, otherwise it answers the incoming message with a message containing the better value. Then it checks a proof of work that must be included in the incoming message, to ensure that the other peer's ID is not made up (otherwise a malicious peer could claim to have an ID of exactly the target value every round). Once validated, it compares the brodcast time of the received value with the current time and if it's not too early, sends the received value to its neighbors. Otherwise it stores the value until the correct broadcast time comes. This prevents unnecessary traffic of sub-optimal values, since a better value can come before the broadcast time, rendering the previous one obsolete and saving the traffic that would have been used to broadcast it to the neighbors.

Calculating the estimate

Once the closest ID has been spread across the network each peer gets the exact distance betweed this ID and the target value of the round and calculates the estimate with a mathematical formula described in the tech report. The estimate generated with this method for a single round is not very precise. Remember the case of the example, where the only peer is the ID 44 and we happen to generate the target value 42, thinking there are 50 peers in the network. Therefore, the NSE subsystem remembers the last 64 estimates and calculates an average over them, giving a result of which usually has one bit of uncertainty (the real size could be half of the estimate or twice as much). Note that the actual network size is calculated in powers of two of the raw input, thus one bit of uncertainty means a factor of two in the size estimate.