Third Party Bit Commitment

posted by Craig Gidney on January 22, 2014

In this post: using a trusted third party to make bit commitment perfectly secure.


I wrote my master’s thesis on the subject of secret sharing, where a dealer splits some secret message into shares. Later on, players use some fraction of the shares to recover the secret. One of the tweaks to the problem that I addressed was “What if the players, who want to backstab each other, have infinite compute power? Can it still be done fairly?”.

Giving the players unbounded compute power makes things tricky because a lot of cryptographic primitives rely on computational hardness for their security. Bit commitment is an example of such a primitive, and is important in secret sharing protocols for keeping players honest.

The bad news is that there is no traditional bit commitment scheme, where a sender commits to a message they will later give a receiver, that is not based on computational hardness. Not even for quantum senders and receivers. The good news is that the secret sharing use case is slightly different because our protocol starts with a trusted third party (the dealer) creating the shares.

Having a dealer introduces the ability to give the receiver data that the sender does not know. The dealer can add some redundancy to the messages, and tell the receiver how the redundancy should relate to the message. As long as changing the message scrambles that relation, the sender can’t change the message without risking detection. As long as the relation doesn’t reveal the message, the receiver can’t predict the message. It’s just a matter of doing it in a nice way.

Shapes in Space

When I started on this bit-commitment-via-redundancy idea I was thinking in terms of parity bits and checksums, but soon I settled on the idea of putting a point on a line. I would encode the message into one of the components making up a line, and give the receiver a random point on the line.

The idea is that a single point is not enough to recover a line (many possible lines pass through any given point), and this prevents the receiver from recovering the message. Also, because lines only intersect at one point (or zero when parallel), a sender changing the message will fail to maintain all but one of the many possible verification points. If the sender guesses wrong when choosing which point to keep, they get caught.

Note that we can’t use “normal” lines (Euclidean space, real coordinates), because they would leak information to the receiver. For example, suppose there’s a maximum slope of 1000, a maximum y-intercept of 1000, and the receiver gets the point (2, 3000). There’s only one way for that to happen, so the receiver will learn the message without any help from the sender. If we don’t put maximum values on the slope or y-intercept, and instead make larger values less and less likely, then the receiver won’t know but they’ll still get a pretty good idea. There will be a loss of information entropy. What we want to use instead are lines over finite fields (e.g. modular arithmetic with a prime modulus).

Finite fields can take some getting used to, but they’re surprisingly similar to the field of real numbers. Non-parallel lines still intersect at exactly one point, parallel lines still intersect at zero points, and any two points on a line still uniquely determine that line. To give you an idea of how they work, I’ve made an animation showing how the line between two points changes as one of the points is moved:

Varying target, solving slope

Notice how, in the above animation, the line loops from top to bottom and from right to left. Lines in finite fields are loopier. Also notice how points always fall on an intersection, corresponding to a pair of integers \pmod{13}. The space is discrete, not continuous. Although I’ve drawn the lines as continuous, that’s just to make them recognizable as lines instead of a confusing sequence of discrete points.

Getting back to bit commitment, the above diagram shows the difficulties a sender will have when changing the message. Every time the verification point (the blue one) moves, a different slope is needed to hit it.

Let’s visualize why the receiver is unable to recover the message. The following animation shows that many possible lines (one per possible slope) pass through any single verification point:

Varying slope, solving y

If you keep track of what y-intercepts show up as the slope is incremented in the above animation, you can see that each y-intercept is also used exactly once. Here’s the same animation, but re-ordered so the y-intercept is incrementing instead of the slope incrementing:

Varying y, solving slope

The verification point defines a one-to-one relation between the y-intercepts and the slopes. The trick, that keeps the sender honest, is that each verification point defines a different relationship. The sender doesn’t know which one to maintain.


My initial idea for how a message should be encoded into a line was to make the y-intercept equal to the message and the slope a random number. This works, but has a minor flaw: the query point can’t be placed on x=0 because then the verifier would know the message. This makes it impossible to work modulo 2, which would be convenient considering that computers work in binary. Later I realized I’d just had the roles backwards. The message should determine the slope, and then you randomize the y-intercept.

Here’s the process the dealer uses to commit a sender to sending a specific message to a receiver:

  • We work in a finite field \mathbb{F} large enough to encode the message.
  • Let s be the message, encoded into \mathbb{F}.
  • Pick a random y-intercept y_0 from \mathbb{F}
  • Pick a random query location x_q from \mathbb{F}.
  • Let y_q be the query answer y_0 + s \cdot x_q.
  • Give (y_0, s) to sender.
  • Give (x_q, y_q) to receiver.

When the time comes, the sender will transmit y_0 and s to the verifier. The verifier will check that y_q = y_0 + s \cdot x_q, rejecting the message if the equality does not hold. Senders who choose to forge a message have a \frac{1}{|\mathbb{F}|} chance of succeeding. This can be made arbitrarily small by picking a large enough finite field.

Let’s run through a quick example. Suppose we choose \mathbb{F} to be GF(2^2). The finite field GF(2^2) is like the integers modulo 2, but twice. It has four elements (00, 01, 10, and 11), each corresponding to two bits. Addition is done by bitwise xor-ing (e.g. 11 + 01 = 10), and multiplication by and-ing.

Suppose our message is s = 01. The dealer picks a random y-intercept y_0 = 00 and a random query location x_q = 11. The dealer computes y_q = y_0 + s \cdot x_q = 00 + 01 \cdot 11 = 00 + 01 = 01. The sender gets (y_0=00, s=01) and the receiver gets (x_q=11, y_q=01).

If the sender tries to change the message to 10 and sticks with the same y-intercept, the receiver will check if 01 \stackrel{?}{=} 00 + 10 \cdot 11 and find that 01 \neq 10, detecting the forgery. The “correct” y-intercept to fool the receiver would have been 11, but if the verification location had been x_q=00 then keeping the y-intercept the same would have been the right move.


I was really happy to have stumbled onto this third party bit commitment idea. Crypto is usually terribly complicated, yet we ended up with something so wonderfully simple. It could reasonably have been a tiny paper of its own, separate from my thesis.

Only… there’s this thing that always happens when I come up with ideas. A little searching confirmed it: already discovered.

In 1999, Ron Rivest of RSA fame published Unconditionally secure commitment and oblivious transfer schemes using private channels and a trusted initializer. It describes exactly the system I had found.

At least this time it wasn’t known before I was even born.


Adding a trusted dealer makes bit commitment simple.

Normally bit commitment’s security requires computational hardness assumptions, but the third party allows information-theoretic security.

Ron Rivest is smarter than me.

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My Twitter: @CraigGidney

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