You are collecting your own entropy, and you need to determine when you have collected enough data to use the entropy.
At the highest level, the solution is to be incredibly conservative in entropy estimation. In the discussion, we will examine general practices and guidelines for particular sources.
Fundamentally, the practical way to look at entropy is as a measurement of how much information in a piece of "random" data an attacker can glean about your randomness infrastructure. For example, if you have a trusted channel where you get 128 bits of data, the question we are really asking is this: how much of that data is provided to an attacker through whatever data channels are available to him? The complexity of an attack is based on how much data an attacker has to guess.
Clearly, in the practical sense, a single piece of data can have different amounts of entropy for different people. For example, suppose that we use the machine boot time to the nearest second as a source of entropy. An attacker who has information about the system startup time narrowing it down to the nearest week still has a much harder problem than an attacker who can narrow it down to a 10-second period. The second attacker can try all 10 possible starting values and see if he gets the correct value. The first has far, far more values to try before finding the original value.
In practice, it turns out that boot time is often an even more horrible source of entropy than we have already suggested. The nmap tool can often give the system uptime of a remote host with little effort, although this depends on the operating system and the firewall configuration of the host being targeted.
The basic lesson here is that, before you decide how to estimate entropy, you should figure out what your threat model is. That is, what kinds of attacks are you worried about? For example, it is possible to monitor electromagnetic signals coming from a computer to capture every signal coming from that machine. The CIA has been known to do this with great success. In such a case, there may be absolutely no entropy at all without some sort of measures to prevent against such attacks.
Most people are not worried about such a threat model because the attack requires a high degree of skill. In addition, it generally requires placing another machine in close proximity to the machine being targeted. A more realistic assumption, is that someone with a local (nonroot) account on the machine will try to launch an attack. Quite a bit of the entropy an interactive Unix system typically gathers can be observed by such an attacker, either directly or indirectly.
If you are not worried about people with access to the local system, we believe you should at least assume that attackers will somehow find their way onto the same network segment as the machine that's collecting entropy. You should therefore assume that there is little entropy to be had in network traffic that the machine receives, because other machines on the network may be able to see the same traffic, and even inject new traffic.
Another threat you might want to consider is the possibility of an attacker's finding a way to pollute or destroy one or more entropy sources. For example, suppose you are using a hardware random number generator. The attacker may not have local account access and may not have the resources or know-how for an electromagnetic signal capture attack. However, there may be an easy way to break the physical random number generator and get it to produce a big string of zeros.
Certainly, you can use FIPS 140 testing as a preventive measure here, as discussed in Recipe 11.18. However, those tests are not very reliable. You might still want to assume that entropy sources may not provide any entropy at all.
Such attacks are probably worst-case in most practical systems. You can prevent against tainted entropy sources by using multiple entropy sources, under the assumption (which is probably pretty reasonable in practice) that an attacker will not have the resources to effectively taint more than one source at once.
With such an assumption, you can estimate entropy as if such attacks are not possible, then subtract out the entropy estimate for the most plentiful entropy source. For example, suppose that you want to collect a 128-bit seed, and you read keyboard input and also read separately from a fast hardware random number generator. With such a metric, you would assume that the hardware source (very likely to be the most plentiful) is providing no entropy. Therefore, you refuse to believe that you have enough entropy until your entropy estimate for the keyboard is 128 bits.
You can come up with more liberal metrics. For example, suppose you are collecting a 128-bit seed. You could have a metric that says you will believe you really have 128 bits of entropy when you have collected at least 160 bits total, where at least 80 of those bits are from sources other than the fastest source. This is a reasonable metric, because even if a source does fail completely, you should end up with 80 bits of security on a 128-bit value, which is generally considered impractical to attack. (Thus, 80-bit symmetric keys are often considered more than good enough for all current security needs.)
Now assume that the attacker cannot make a source fail; she can only take measurements for guessing attacks. We will talk about estimating the amount of entropy in a piece of data, assuming two different threat models: with the first, the attacker has local but nonprivileged access to the machine, and in the second, the attacker has access to the local network segment.
 If an attacker already has privileged access to a machine, you probably have more important issues than her guessing random numbers.
In the second threat model, assume this attacker can see everything external that goes on with the application by somehow snooping network traffic. In addition, assume that the attacker knows all about the operational environment of the machine on which the application runs. For example, assume that she knows the operating system, the applications running on the system, approximately when the machine rebooted, and so on. These assumptions mean that a savvy attacker can actually figure out a fair amount about the machine's state from observing network traffic.
Unfortunately, the first problem we encounter when trying to estimate entropy is that, while there is an information-theoretic approach to doing so, it is actually ridiculously difficult to do in practice. Basically, we can model how much entropy is in data only once we have a complete understanding of that data, as well as a complete understanding of all possible channels available to an attacker for measuring the parts of that data that the attacker would not otherwise be able to figure out from patterns in the data.
Particularly when an attacker may have local access to a machine, it can be a hopeless task to figure out what all the possible channels are. Making things difficult is the fact that machines behave very deterministically. This behavior means that the only points where there is the possibility for any entropy at all is when outside inputs are added to the system.
The next problem is that, while a trusted entropy accumulator might be able to take some measurements of the outside data, there may be nothing stopping an attacker from taking measurements of the exact same data. For example, suppose that an operating system uses keyboard strokes as an entropy source. The kernel measures the keystroke and the timestamp associated with the key press. An attacker may not be able to measure keystrokes generated by other users, but he should be able to add his own keystrokes, which the operating system will assume is entropy. The attacker can also take his own timestamps, and they will be highly correlated to the timestamps the operating system takes.
If we need to use our own entropy-gathering on a system that does its own, we trust the operating system's infrastructure, and we use a different infrastructure (particularly in terms of the cryptographic design), measuring entropy that the system also measures will generally not be a problem.
For example, suppose that you have a user interactively type data on startup so that you can be sure there is sufficient entropy for a seed. If an attacker is a local nonprivileged user, you can hope that the exact timings and values of key-press information will contain some data the attacker cannot know and will need to guess. If the system's entropy collection system does its job properly, cryptographically postprocessing entropy and processing it only in large chunks, there should be no practical way to use system infrastructure as a channel of information on the internal state of your own infrastructure. This falls apart only when the cryptography in use is broken, or when entropy estimates are too low.
The worst-case scenario for collecting entropy is generally a headless server. On such a machine, there is often very little trustworthy entropy coming from the environment, because all input comes from the network, which should generally be largely untrusted. Such systems are more likely to request entropy frequently for things like key generation. Because there is generally little entropy available on such machines, resource starvation attacks can be a major problem when there are frequent entropy requests.
There are two solutions to this problem. The first is operational: get a good hardware random number generator and use it. The second is to make sure that you do not frequently require entropy. Instead, be willing to fall back on cryptographic pseudo-randomness, as discussed in Recipe 11.5.
If you take the second approach, you will only need to worry about collecting entropy at startup time, which may be feasible to do interactively. Alternatively, if you use a seed file, you can just collect entropy at install time, at which point interacting with the person performing the install is not a problem.
For every piece of data that you think has entropy, you can try to get additional entropy by mixing a timestamp into your entropy state, where the timestamp corresponds to the time at which the data was processed.
One good thing here is that modern processors can generate very high-resolution timestamps. For example, the x86 RDTSC instruction has granularity related to the clock speed of the processor. The problem is that the end user often does not see anywhere near the maximum resolution from a timing source. In particular, processor clocks are usually running in lockstep with much slower bus clocks, which in turn are running in lockstep with peripheral clocks. Expert real-world analysis of event timings modulo these clock multiples suggests that much of this resolution is not random.
Therefore, you should always assume that your clock samples are no more accurate than the sampling speed of the input source, not the processor. For example, keyboards and mice generally use a clock that runs around 1 Khz, a far cry from the speed of the RDTSC clock.
Another issue with the clock is something known as a back-to-back attack, in which depending on the details of entropy events, an attacker may be able to force entropy events to happen at particular moments. For example, back-to-back short network packets can keep a machine from processing keyboard or mouse interrupts until the precise time it is done servicing a packet, which a remote attacker can measure by observing the change in response in the packets he sends.
To solve this problem, assume that you get no entropy when the delta between two events is close to the interrupt latency time. That works because both network packets and keystrokes will cause an interrupt.
 Some operating systems can mitigate this problem, if supported by the NIC.
Timing data is generally analyzed by examining the difference between two samples. Generally, the difference between two samples will not be uniformly distributed. For example, when looking at multiple such deltas, the high-order bits will usually be the same. The floor of the base 2 logarithm of the delta would be the theoretical maximum entropy you could get from a single timestamp, measured in bits. For example, if your delta between two timestamps were, in hex, 0x9B (decimal 155), the maximum number of bits of entropy you could possibly have is 7, because the log of 155 is about 7.28.
However, in practice, even that number is too high by a bit, because we always know that the most significant bit we count is a 1. Only the rest of the data is really likely to store entropy.
In practice, to calculate the maximum amount of entropy we believe we may have in the delta, we find the most significant 1 bit in the value and count the number of bits from that point forward. For example, there are five bits following the most significant 1 bit in 0x9B, so we would count six. This is the same as taking the floor of the log, then subtracting one.
Because of the nonuniform nature of the data, we are only going to get some portion of the total possible entropy from that timestamp. Therefore, for a difference of 0x9B, six bits is an overestimate. With some reasonable assumptions about the data, we can be sure that there is at least one fewer bit of entropy.
In practice, the problem with this approximation is that an attacker may be able to figure out the more significant bits by observation, particularly in a very liberal threat model, where all threats come from the network.
For example, suppose you're timing the entropy between keystrokes, but the keystrokes come from a computer on the network. Even if those keystrokes are protected by encryption, an attacker on the network will know approximately when each keystroke enters the system.
In practice, the latency of the network and the host operating system generally provides a tiny bit of entropy. On a Pentium 4 using RDTSC, we would never estimate this amount at above 2.5 bits for any application. However, if you can afford not to do so, we recommend you do not count it.
The time where you may want to count it is if you are gathering input from a source where the source might actually come from a secure channel over the network (such as a keyboard attacked to a remote terminal), and you are willing to be somewhat liberal in your threat model with respect to the network. In such a case, we might estimate a flat three bits of entropy per character, which would include the actual entropy in the value of that character.
 Assuming that successive characters are different; otherwise, we would estimate zero bits of entropy.
In summary, our recommendations for timestamps are as follows:
Keep deltas between timestamps. Do not count any entropy for the first timestamp, then estimate entropy as the number of bits to the right of the most significant bit in the delta, minus one.
Only count entropy when the attacker does not have a chance of observing the timing information, whether directly or indirectly. For example, if you are timing entropy between keystrokes, be sure that the typing is done on the physical console, instead of over a network.
If you have to accept data from the network, make sure that it is likely to have some other entropy beyond the timing, and never estimate more than 2.5 bits of entropy per packet with a high-resolution clock (i.e., one running in the GHz range). If your clock has better than millisecond resolution and the processor is modern, it is probably reasonable to assume a half-bit of entropy on incoming network packets.
As with any entropy source, when you are trying to get entropy from a key press, you should try to get entropy by taking a timestamp alongside the key press and estimate entropy as discussed in the previous subsection.
How much entropy should you count for the actual value of the key itself, though?
Of course, in practice, the answer has to do with how likely an attacker is to guess the key you are typing. If the attacker knows that the victim is typing War and Peace, there would be very little entropy (the only entropy would be from mistakes in typing or time between timestrokes).
If you are not worried about attacks from local users, we believe that a good, conservative approximation is one bit of entropy per character, if and only if the character is not identical to the previous character (otherwise, estimate zero). This assumes that the attacker has a pretty good but not exact idea of the content being typed.
If an attacker who is sending his own data into your entropy infrastructure is part of your threat model, we think the above metric is too liberal. If your infrastructure is multiuser, where the users are separated from each other, use a metric similar to the ones we discussed earlier for dealing with a single tainted data source.
For example, suppose that you collect keystroke data from two users, Alice and Bob. Keep track of the number of characters Alice types and the number Bob types. Your estimate as to the number of bits of entropy you have collected should be the minimum of those two values. That way, if Bob is an attacker, Alice will still have a reasonable amount of entropy, and vice versa.
If you are worried that an attacker may be feeding you all your input keystrokes, you should count no entropy, but mix in the key value to your entropy state anyway. In such a case, it might be reasonable to count a tiny bit of entropy from an associated timestamp if and only if the keystroke comes from the network. If the attacker may be local, do not assume there is any entropy.
On most operating systems, moving the mouse produces events that give positional information about the mouse. In some cases, any user on the operating system can see those events. Therefore, if attacks from local users are in your threat model, you should not assume any entropy.
However, if you have a more liberal threat model, there may be some entropy in the position of the mouse. Unfortunately, most mouse movements follow simple trajectories with very little entropy. The most entropy occurs when the pointer reaches the general vicinity of its destination, and starts to slow down to lock in on a target. There is also often a fair bit of entropy on startup. The in-between motion is usually fairly predictable. Nonetheless, if local attacks are not in your threat model, and the attacker can only guess approximately what parts of your screen the mouse went to in a particular time frame based on observing program behavior, there is potentially a fair bit of entropy in each mouse event, because the attacker will not be able to guess to the pixel where the cursor is at any given moment.
For mouse movements, beyond the entropy you count for timestamping any mouse events, we recommend the following:
If the mouse event is generated from the local console, not from a remotely controlled mouse, and if local attacks are not in your threat model, add the entire mouse event to your entropy state and estimate no more than three bits of entropy per sample (1.5 would be a good, highly conservative estimate).
If the local user may be a threat and can see mouse events, estimate zero bits.
If the local user may be a threat but should not be able to see the actual mouse events, estimate no more than one bit of entropy per sample.
Many people believe that measuring how long it takes to access a disk is a good way to get some entropy. The idea is that there is entropy arising from turbulence between the disk head and the platter.
We recommend against using this method altogether.
There are several reasons that we make this recommendation. First, if that entropy is present at all, caching tends to make it inaccessible to the programmer. Second, in 1994, experts estimated that such a source was perhaps capable of producing about 100 bits of entropy per minute, if you can circumvent the caching problem. However, that value has almost certainly gone down with every generation of drives since then.
As we have mentioned previously in this recipe, while it may be tempting to try to gather entropy from network data, it is very risky to do so, because in any reasonable threat model, an attacker can measure and potentially inject data while on the network.
If there is any entropy to be had at all, it will largely come from the entropy on the recipient's machine, more than the network. If you absolutely have to measure entropy from such a source, never estimate more than 2.5 bits of entropy per packet with a high-resolution clock (i.e., one running in the GHz range). If your clock has better than millisecond resolution and the processor is modern, it is probably reasonable to assume a half-bit of entropy on incoming network packets, even if the packets are generated by an attacker.
There is generally some entropy to be had by reading a sound card just from random thermal noise. However, the amount varies depending on the hardware. Sound cards are usually also subject to RF interference. Although that is generally not random, it does tend to amplify thermal noise.
Conservatively, if a machine has a sound card, and its outputs do not fail FIPS-140 tests, we believe it is reasonable to estimate 0.25 bits per sample, as long as an attacker cannot measure the same samples. Otherwise, do not estimate any.
Systems effectively gain entropy based on inputs from the environment. So far, we have discussed how to estimate entropy by directly sampling the input sources. If you wish to measure entropy that you are not specifically sampling, it is generally feasible to query system state that is sensitive to external inputs.
In practice, if you are worried about local attacks, you should not try to measure system state indirectly, particularly as an unprivileged user. For anything you can do to measure system state, an attacker can probably get correlated data and use it to attack your results.
Otherwise, the amount of entropy you get definitely depends on the amount of information an attacker can guess about your source. It is popular to use the output of commands such as ps, but such sources are actually a lot more predictable than most people think.
Instead, we recommend trying to perform actions that are likely to be indirectly affected by everything going on in the system. For example, you might measure how many times it takes to yield the scheduler a fixed number of times. More portably, you can do the same thing by timing how long it takes to start and stop a significant number of threads.
Again, this works only if local users are not in your threat model. If they are not, you can estimate entropy by looking at the difference between timestamps, as discussed earlier in this recipe. If you want to be conservative in your estimates, which is a good idea, particularly if you might be gathering the same entropy from different sources, you may want to divide the basic estimate by two or more.
Recipe 11.5, Recipe 11.18