Finessing commitments


This post was mostly prompted by a long series of discussions had online and in person with many people, including in particular Adam Back and Tim Ruffing (but lots of others!) - and certainly not restricted to discussions I took part in - about the tradeoffs in a version of Bitcoin that does actually use Confidential Transactions.

The topic that keeps recurring is: exactly what level of safety against hidden inflation does CT offer, in principle and in practice; this is closely related to what level of privacy it offers, too, but the hidden inflation is what gets people thinking, first and foremost.

My goal here is to explain to people who are not completely up to speed with what causes this discussion; to explain, in detail but without assuming too much pre-knowledge, what the heart of the tradeoff is, and in the last couple of sections, how we might get around it. How we might "have our cake and eat it" so to speak.

You'll find a lot of the ideas in first three sections of this blog post, although not all of them, in my write up on Bulletproofs section 3, and I also went over the basics in the early part of my London talk on the Schnorr signature (since they're not visible, you'd want to see the slides for that talk if you watch it).

You should have some general idea about what Confidential Transactions is, in order to understand the second half - in particular you should understand that amounts are hidden under Pedersen commitments, although we'll go through some of it here in the early sections.

Commitments - the basic ideas

A commitment fixes a value in advance, without revealing it. Think of flipping a coin and covering it with your hand as part of a gamble or game. Covering it with your hand means it's invisible. The visibility of your hand not moving, and pressed against a surface over the coin, on the other hand, ensures you can't cheat, because you can't flip the coin under your hand. This is the physical representation of the two ideas of a commitment; everything that cryptographers call a commitment has those two properties, but defended by very strong mathematical "guarantees" (more on those quote marks shortly!), instead of by a crappy physical analogue like a pressed-down hand:

  • Hiding - nobody but the committer can see or infer the actual value being committed
  • Binding - the committer can't change the value after the commitment is published.

The most "vanilla" way to implement this primitive is to use a cryptographically secure hash function, say \(\mathbb{H}\). You'd make up a random number \(r\) and combine it with your secret value \(x\), and the commitment would just be something like \(\mathbb{H}(x||r)\), where || indicates just concatenating the data together.

For background, commitments are usually defined in the literature as a tuple of three algorithms: Setup, Commit and Verify/Open, where the last one needs the committing party to give what's called the "opening" of the commitment as input. It should be clear what is meant when I say that the "opening" of the above commitment is just literally the two values \(x\) and \(r\). (It's like taking your hand off the coin in our analogue).

Because (cryptographically secure) hash-functions are collision-resistant (avoiding technical definitions for brevity here), you can't open a commitment you already made to \(x\), with a different value \(x'\), i.e. as committer you can't cheat, because even though you're free to make your cheating opening operation with any \(r'\) you like (it wasn't published in advance), you just can't find any combination \(x',r'\) such that \(\mathbb{H}(x||r)=\mathbb{H}(x'||r')\). That's why you need a proper, strong hash function - to make that computationally infeasible.

Homomorphic commitments

The strict definition of homomorphism requires going into group theory a bit, which isn't needed here, but basically a homomorphism should be thought of as a function that takes you from one group into another while keeping the group operation intact (so it's closely related to symmetry). A toy example would be the homomorphism from the group of integers under addition (\(\mathbb{Z},+)\) to the group of integers modulo two \(\mathbb{Z}_2\) (a very simple group!: two members: 0, 1; each is self-inverse; 0+1=1+0 = 1, 0+0=1+1 =0). What is that homomorphism? It's just a function that returns 0 if the input integer is even, and 1 if the input integer is odd. We lost everything about the integers except their evenness/oddness but we kept the group operation. Call that function \(f()\) and we clearly have \(f(a+b)=f(a)+f(b)\) because even + odd = odd, odd + even = odd, even + even = odd + odd = even.

But homomorphisms need not collapse a larger group into a smaller one, the "throw away everything except X" can still conceivably mean throwing away nothing, i.e. mapping from one group to another with the same order - and the homomorphism that's relevant to this discussion fits that description: it translates members of the group of integers modulo \(N\) under addition, to the group of elliptic curve points of order \(N\), under elliptic curve point addition: \(a \in \mathbb{Z}_N \ ; a \rightarrow aG\), where \(G\) is the so-called generator point of the elliptic curve (exactly which point on the curve is taken for this role is irrelevant, but the definition has to include one, and we call it \(G\)), and implicit in the notation \(aG\) is the fact that it means a scalar multiplication of \(G\) by \(a\), i.e. \(G\) added to itself \(a\) times.

In Bitcoin, the curve in question is secp256k1, but that's not important here (except perhaps the fact that \(N\) is prime, meaning both groups are isomorphic to the cyclic group of order \(N\)).

So this is all getting technical, but all it really boils down to is \((a+b)G = aG + bG\) is an equation that holds, here.

What about commitments? We can treat the above homomorphism as similar to a cryptographic hash function, in that we can assume that it's not possible to derive the integer \(a\) given only the curve point \(aG\) - this assumes that the "elliptic curve discrete logarithm problem" is hard (ECDLP for short; also, that's kind of the definition of that problem).

In making that assumption, we can go ahead and apply the same paradigm: take a random \(r\) for hiding, then take a second generator point \(H\) and write our commitment as \(xG + rH\) (addition not concatenation; publishing the two curve points separately would defeat the purpose; \(r\) is supposed to be helping to hide \(x\)!).

And we note how the homomorphism between an integer and the scalar multiple of \(G\) by that integer carries over to this composite case of the sum of two points: \(x_{1}G + r_{1}H + x_{2}G + r_{2}H = (x_{1}+x_{2})G + (r_{1}+r_{2})H\).

So it means the commitments, which we can denote \(C(x, r)\) for brevity, are homomorphic too. The sum of two commitments is equal to the commitment to the sum (in this sentence, notice how "sum" refers to the sum of two integers; but the homomorphism allows that to "carry over" to "sum" as in elliptic curve point addition). Algebraically we can condense it to: \(C(x_1,r_1)+C(x_2,r_2) = C(x_{1}+x_{2},r_{1}+r_{2})\).

This specific form of commitment is known as the Pedersen commitment. It is most commonly referred to in the finite field, non elliptic curve form (where \(C=g^{x}h^{r}\) rather than \(C=xG + rH\)), but it's the same thing.

And to re-emphasise what at this point should be obvious - none of the above applies to commitments built with cryptographic hash functions.

Imperfect commitments

The first imperfection in the idea above of the Pedersen commitment: the second generator point \(H\). In practice it has been calculated in a NUMS way, using some kind of hash of the defined generator point \(G\). The idea is that if this hash function \(\mathbb{H}\) is secure as described above, \(H\) cannot be reverse-engineered such that its discrete log (that's to say, \(\gamma\) s.t. \(H=\gamma G\)). And while this seems like a side-note, we can use this to lead in to the subtleties which are the main topic of this blog post.

Consider (and this is an excellent exercise for those somewhat familiar with basic elliptic curve operations as used in Bitcoin and similar, but not yet seasoned in it): if you secretly knew that \(\gamma\) and no one else did, and Pedersen commitments were being used, how could you use this knowledge to gain advantage?

(Spoiler space)





The answer is that the a commitment would lose its binding property. Remember: the binding property is the defence the receiver/verifier of the commitment has against the creator/committer. So you would be able to make a commitment randomly - just take any random "private key", call it \(y\), and publish its "public key" point \(yG\) as \(C\). Then when asked to verify later, you could pretend that you had bound this commitment to any \(x\) as follows: Take your newly chosen \(x\), and calculate what \(r\) has to be for that to work:

\(xG + rH = C\) \(xG + r\gamma G = yG\) \(\therefore\) \(r = \gamma^{-1}(y-x)\)

There are two thoughts that may spring out of this:

  • A Pedersen commitment then, is only sound in as much as the relative discrete log between \(H\) and \(G\) is unknown. And as is often discussed, the aforementioned ECDLP is almost certainly broken by a sufficiently powerful quantum computer (although there might conceivably other ways to compute a discrete log quickly, as yet unknown to mathematicians). We say that Pedersen commitments are only computationally binding - this means, only binding inasmuch as breaking their binding requires an unrealistic amount of computational power - to an adversary with infinite computing power, they are not binding, at all.
  • You may think - what about hiding? Surely knowing this "secret unlocking key" \(\gamma\) can break that as well. The answer is an emphatic no - but what might be really surprising is: the answer is no in the case of hiding for exactly the same reason that the answer is yes, in the case of binding!

I'm first going to explain that formally, but then we'll take a step back, and use a picture and perhaps some examples to flesh out what's really going on here, because it's the heart of the story we're telling.

The reason hiding is not lost is because an adversary on the receiving/verifying side who wants to "sneak peek" inside \(C\) to find out the real \(x\) faces an insurmountable problem: there isn't only one answer!. If the answer is \(x\) then the \(r\) value is, as already explained, \(\gamma^{-1}(y-x)\). Remember, a computationally unbound attacker can find those discrete logs - can get \(y\) from \(C\), and can get \(\gamma\) from \(H\). So any \(x\) will have a corresponding \(r\). And of course you can flip it backwards - if he fixes a particular \(r\) he can get the corresponding \(x\). Where is this slipperiness coming from? What's it's fundamental cause?

Think about functions. They take inputs to outputs of course, and we generalise by talking about input spaces and output spaces (by "space" here I mean something like "the set of all..."). The technical terms used are domain/range and sometimes preimage space and image space. The input space for the private->public key "function" in Bitcoin is of course the set of all integers modulo \(N\). And then output space is the set of all public keys, which is the set of all points on the curve secp256k1. Here we have what's called a "one-one" mapping (technically it's both "one-one" and also "onto", since all points in the output space are mapped to). But only "nice" functions are like that, not all are. Some, in particular, have more than one input corresponding to the same output.

And that's exactly what's happening with the Pedersen commitment; moreover, there's a very concrete reason why the Pedersen commitment has that property of having multiple inputs for the same output - it logically has to! Consider this diagram:

Input space larger than output space

By the pigeonhole principle, because there are more inputs than outputs, it's impossible for it not to be the case that at least some outputs have more than one corresponding input - they wouldn't all "fit" otherwise. And it's for this reason that a commitment scheme where the input space is larger than the output space can have perfect hiding. (notice "can", not "will" - for the hiding to be *perfect* we need to be leaking zero information about which element of the input space is being hidden; that needs it to be the case that we can demonstrate that *any* element of some very large subset of the input space is part of a valid opening of a given commitment \(C\); that's true here in Pedersen, but certainly not for some other commitment schemes).

And for the exact same reason, the binding is only computational - there are bound to be at least some outputs with more than one input, and so if nothing else, by exhaustive search, the computationally unbounded attacker can simply search and find the other input corresponding to the same output (as per the diagram, if the attacker had the value \(k\) as commitment, he could find \(x_2, r_2\) even if the commitment was originally to \(x_1, r_1\). At least that'll be true for some outputs (in Pedersen, for every output, in fact).

So the Pedersen commitment falls neatly into this category; it has an input space of 512 bits if the message \(x\) and randomness \(r\) are both the same size as the group elements and an output space of 256 bits (almost exactly); the outputs \(C\) are the points on the secp256k1 curve.

(You'll notice how technically "exhaustive search" may not be the actual process - there can be shortcuts depending on how the commitment is structured; in the case of Pedersen, because it hinges on the unknown discrete log \(\gamma\), the attacker can leverage that - he can use that knowledge to find these "other inputs" directly instead of by exhaustive search).

What if the input space is smaller than the output space? It takes a moment of reflection to realise that this idea doesn't make sense. A function has a single output by definition (note that doesn't mean the output can't be multiple "things", e.g. it could be 4 integers or 6 curve points or 5 triangles, whatever - but each of them is a single output). So a function with 10 inputs can't have more than 10 outputs. Which means we have one case remaining:

What if the input space is the same size as the output space?

Input and output space equal size

In this case we must have a one-one mapping - again by the pigeonhole principle (Remember, we are defining the output space as the space of actual possible outputs, not some larger set; this will usually require justification - you can justify it here by first considering the second commitment point \(C_2=rG\) - note that the lines are horizontal for a reason!). And by the same reasoning as above, but in reverse, we see that this gives perfect binding, and at best computational (imperfect) hiding.

What's neat about this reasoning is that none of it is specific to anything elliptic curve, or discrete log related, or anything - it applies to any commitment scheme, including the hash-based one we introduced right at the start. The only difference between the Pedersen and hash-based case is because of the messiness mathematically of hash functions, we can't really talk about perfection; it's only the limitations, the negative parts of the above logic, that are the same:

If your output space is the space of SHA256 outputs, then it's 256 bits. Now according to specification, that hash function can take extremely large input (I forget exactly, but vastly larger than 256 bits), which means it is in the first category - its input space is vastly larger than its output space, so it cannot be perfectly binding. But that doesn't mean that it's perfectly hiding, unfortunately - that would require that a given output leaks precisely zero information about the corresponding input. But it's certainly not the case that we have some theorem about SHA256 that ensures that every input is equiprobable, given an arbitrary output. So at best we have computational hiding, and that's based on the idea tha the hash function is well designed. See Wikipedia for a reminder on the key properties of cryptographic hash functions. These properties are also what provides the argument for at least a computational binding. But again, it's certainly not perfectly either hiding or binding.

So let's summarize the key insight:

It's LOGICALLY impossible for a commitment scheme to be both perfectly hiding and perfectly binding, no matter what algorithm or mathematical architecture is used to construct it.

Why "logically"? Because we've demonstrated the two ideas are fundamental contradictions of each other; it is only confusion to think you can get both at the same time. Another way to say it (slightly more dynamic description):

A commitment scheme which has been constructed to have perfect binding will at BEST achieve computational hiding, while a scheme constructed to achieve perfect hiding will at BEST achieve computational binding.

Here we're emphasizing that these are the limits, only achieved by well designed algorithms; a badly designed or not-fit-for-purpose commitment scheme may not be perfect in either sense, and for example may not even manage to be computationally hiding, e.g. an adversary may very feasibly be able to break the hiding property without excessive computational resources. This is just a description of the best we can do.

From hidden to bound

We'll get into the Bitcoin-related application shortly, but for now note that is not unreasonable to prefer binding over hiding in the trade-off. Since clearly Pedersen doesn't fit there, what does?

Let's start with an almost-obvious idea: suppose I want to commit to a value \(x\) and have it be perfectly binding. Can I just use \(C=xG\) as the commitment?

If you've been following along, you'll probably be a little uncertain, because .. the "hiding" part doesn't seem to have been implemented. You're right to be uncertain, because the answer is really "formally no, but it kinda depends".

There are two scenarios: if the set of values you might commit to is restricted in some way, e.g. a number between 1 and \(2^{25}\) then the lack of hiding makes the commitment a complete failure, because a computer could just find it by brute force guessing. And if your \(x\) was a random value in the entire range \(2^{256}\) of elements of the group - this kind of construction is sometimes used as a commitment, but it doesn't count as a proper, generic commitment scheme, because it doesn't have even computational hiding in the general case; if I think I know what your \(x\) is (or know the range of it), I can just check if I'm right; there is no blinding value \(r\) to prevent that.

This naturally leads us to the ElGamal encryption scheme, re-purposed as a commitment scheme (this can be done with any cryptosystem, by the way):

Take our usual suspects \((x, r)\) and construct two elliptic curve points: \((xG+rH, rG)\). This is the ElGamal commitment (with all notation as for Pedersen). Wait, I hear you cry - you're just regurgitating the Pedersen commitment, but adding \(rG\)? What does that mean? Well, we're taking the slightly broken idea above and applying it in conjunction with the idea of the Pedersen commitment. We "commit" to the value \(r\) using \(rG\), and that's OK specifically because \(r\) is a random number in the range (a bit like a "nonce") used just for this commitment, so there is no guessing it outside the standard brute force or breaking ECDLP; by doing so we've increased the output space from Pedersen's set of single curve points to the Cartesian product of 2 sets of curve points. And we by doing so arrive at the second of the two scenarios described in the two diagrams above; now, for each input tuple \((x, r)\), there is an output tuple \((C_1,C_2) = (xG+rH,rG)\) - guaranteed distinct because the mapping from \(r\) to \(rG\) is one-one - so the mapping is one-one and is perfectly binding. More simply: the task of the adversary who wants to break the commmitment by opening it to a different value than originally chosen is now impossible: for \(rG\) there is precisely one and only one \(r\), and once \(r\) is set, there is only one \(x\): it's the discrete log of \(C_1 -rH\) which is now uniquely defined, once \(r\) is.

And, following our insights above, it is now decidely unsurprising to learn that the described ElGamal commitment is only computationally hiding: because \(rG\) is published as a separate curve point \(C_2\) and not folded into the single curve point as with Pedersen, an attacker with the ability to solve the discrete log problem can extract, from that, \(r\) and then follow up by extracting from \(C_1 - rH=xG\), the supposedly hidden committed value \(x\).

But let's be clear: it is computationally hiding, unlike our toy "quasi-commitment" \(xG\) which fails at that task (imagine committing to the value "2"). And that can be expressed formally with what's called a "reduction proof"; a rather weird but also very clever concept often used in cryptography:

The ElGamal commitment is hiding if the DDH problem is hard,
because an adversary who can violate the hiding property of the ElGamal
commitment can use that algorithm to solve the DDH problem.

DDH refers to the Decisional Diffie Hellman problem - in words, it's that you can't distinguish  \(abG\) from a random curve point, even if you already know the values of \(A,B\) where \(A=aG, B=bG\).

The intended consequence of this reasoning (and notice how slippery this logic is!) is to say: DDH is actually hard, therefore ElGamal is computationally hiding. Or: since it is believed that DDH is hard, it follows that "we" (some undefined group of cryptographers) believe that ElGamal, as a commitment scheme, is computationally hiding.

Brief observation: ElGamal is homomorphic

Notice how the description of the homomorphic (with respect to addition) property of the Pedersen commitment cross applies here; even though we have two curve points here, not one, the same linearity exists:

\(C_{EG}(x_1, r_1) + C_{EG}(x_2, r_2) = \)

\((x_1G + r_1H, r_1G) + (x_2G + r_2H, r_2G)\)

\( = ((x_1 + x_2)G + (r_1+r_2)H, (r_1+r_2)G)\)

\( = C_{EG}(x_1+x_2, r_1+r_2)\)

An unpalatable tradeoff?

So all of the above is the "behind the scenes" of the discussion you'll often see in public about Confidential Transactions in Bitcoin, specifically (not that the tradeoff doesn't apply in other systems like Monero of course).

We naturally choose the Pedersen commitment for Confidential Transactions, because it's more compact (remember - size of output space!). It's only one curve point as output. Confidential Transactions take up a non-trivial amount of extra space in a transaction, so it's natural to prefer Pedersen to ElGamal for that reason, even though, importantly, both have the necessary homomorphic property as already outlined.

Moreover (and more importantly, actually), a CT output needs a range proof (as explained in great detail e.g. here, see also bulletproofs e.g. here and here), which itself requires a lot of space - the range proofs described in the link, especially bulletproofs, go to a lot of trouble to condense this data to the greatest extent possible, since it must be published on the blockchain for all to verify, but that space usage is a serious issue.

The previous links point to all the work done on space optimisation for Pedersen; if we switched to ElGamal we'd lose that (I'm not exactly sure where we'd be in terms of how much space a CT style output would take up, but it would definitely be considerably more. While writing this I've noticed Andreev has written up an ElGamal range proof here).

Hence the title of the subsection; our choice in CT for something like Bitcoin seems to be:

  • Continue on the existing path - highly space optimised Pedersen commitments with perfect hiding and computational binding under the ECDLP assumption.
  • Switch to ElGamal commitments, with much more bloaty range proofs and commitments, which however have perfect binding and computational hiding (under DDH assumption).

Some people might argue that there is just too much fuss and worry about this. Computational is good enough, if our crypto hardness assumptions are good enough, and they are kind of industry standard already. However there's a big problem with this reasoning, and it was explained in the "tradeoffs" section of this earlier blog post. To avoid getting sidetracked on that now, let me summarize simply:

A break in the binding assumption of Confidential Transactions can result in the attacker being able to print money in arbitrary amounts at any time, with absolutely no knowledge by the outside world.

As I was at pains to point out in the linked blog post, this problem is not CT-specific; it's generic to any blinding mechanism relying on cryptographic hardness assumptions (i.e. without perfect binding or something analogous where even an infinitely powerful adversary cannot violate the binding of the blinded amount).

But here (for the rest of this blog post) we'll focus specifically on the CT version of the problem.

The unexcluded middle

If perfect binding and hiding are logically incompatible in a commitment, our only choice to violate the principle of the excluded middle is to step outside the boundaries of the problem described, and the most natural way to do that is to use two different commitments.

Using both Pedersen and ElGamal concurrently makes so little sense as to be incoherent, not least because an ElGamal commitment contains a Pedersen commitment. But the key word you could have skipped over in that sentence was concurrently. Ruffing and Malavolta in this paper suggest spreading the problem over time:

Switch commitments

The idea here is deceptively simple: what if you use an ElGamal commitment, but don't verify the non-Pedersen component (the second point \(rG\) to use consistent notation) initially. If there is some time \(T\) at which all participants in the system agree that the ECDLP has "fallen" to quantum computing (the most well discussed failure vector of elliptic curve crypto), it could be required that after that flag day, spending of coins (presumably into some safer new cryptosystem defined by consensus; spending into current-style Bitcoin outputs would probably not make sense, here) is only valid if the verification/opening (and the range proof) were applied to the full ElGamal commitment \(xG+rH, rG\) and not just \(xG+rH\) as was allowed before \(T\).

There are two critiques that may immediately spring to mind, one obvious and one not:

  • Not necessarily a realistic scenario - the break may be very public or not, it may be very gradual or not. Declaring a flag day is mostly assuming it being public. So it's not a panacea.
  • If you've been reading closely all this time, you'll be alert to a serious drawback: publishing an ElGamal commitment will not actually be hiding, if ECDLP is "cracked" (you remember that it requires DDH hardness, but it's easy to see that if you "crack" ECDLP you also crack DDH).

Taking a more positive perspective, though: it's not as if \(T\) has to be a "panic stations day". Just as hash functions and TLS versions are sometimes retired because they start to show just a tiny bit of weakness, it would similarly make perfect sense for Bitcoin to be similarly prompt in making a switch to a post-quantum cryptosystem once EC came into question, and not wait to be attacked. Not to say it would be easy!

This approach is sometimes called "cryptographic agility" - awkward as it seems, we do kinda want the ability to upgrade cryptographic protocols "in-flight", while they are being used.

So at this point we have an ingenious and smart amelioration to the problem, but it can't be called a complete solution, I think - and principally because of the (admittedly tiny) possibility of a private break by some lone genius or similar.

We put a commitment inside your commitment, so ...

The authors and collaborators of the switch commitment paper and idea (Ruffing, Malavolta, Wuille, Poelstra, others .. I'm not actually sure) found a way to slightly improve the properties of such switch commitments: a structure they call the opt-in switch commitment which looks something like this:

\(xG + (r+\mathbb{H}(xG+rH || rG))H = xG + r'H\)

The idea is to tweak the blinding component of a standard Pedersen commitment with the hash of an ElGamal commitment to the same value (insert old meme as appropriate). Those of you aware of such things may instantly recognize a close parallel with ideas like pay-to-contract and taproot (the latter was inspired by the former, so no surprise there). We're effectively committing to a "contract" which here is a promise to open to an ElGamal commitment later, if the consensus calls for it, while for now not revealing that contract, as it's hidden/blinded with the value \(r\).

As noted on the mimblewimble mailing list by Ruffing, this has a couple of very important advantages over the non-opt-in version:

  • It preserves the perfect hiding of the Pedersen commitment for as long as the flag day \(T\) isn't reached (it's exactly a Pedersen commitment until then).
  • It doesn't use up another curve point on the blockchain - you only publish the single curve point as per Pedersen, and not two as per ElGamal.

(Another useful feature - you can derive the value of \(r\) from your private key deterministically to make it more practical).

Of course one must prove it's secure (under the random oracle model) but for now I'll take that as a given (it's too much detail for here). But clearly this is a neat way to encapsulate that "switch" idea; modulo security proofs, it's an unqualified and very substantial improvement over the "naked ElGamal" version.

A hard decision for the sleepy or lazy

There is still an area of imperfection even in this souped-up "opt-in" switch commitment case. After the flag day \(T\) if you still have not moved coins from existing outputs, you can publish the ElGamal "contract" (commitment) inside the hash, thus keeping the binding property, so that the envisioned attacker-possessing-a-quantum-computer will still not be able to print money, but in so doing, you give up the hiding (the value is revealed at least to such attackers because they can break the DDH problem). So thus a person failing to take action before said deadline \(T\) has at least to risk, and probably lose, one of those two: their privacy of amount or their money.

Have your cake and eat it?

Is it possible to do better than such a transition approach, as envisaged in the switch commitments paradigm?

As was earlier discussed, it suffers from not covering every threat scenario, in particular, it does not cover the scenario of a private and unexpected break.

Unfortunately this is where this very long blog post trails off ... because I don't know, and currently I don't think anyone else does.

My personal feeling was that the switch commitment paradigm suggests there might be a way to finesse this tradeoff about using commitments. And it also seems to be something which Adam Back seems to have gone some way to thinking through - the fact that a single commitment scheme can't provide perfect hiding and binding for a single value doesn't imply that it is impossible to get this property, as long you're not working with the same value. For example, what if you could provide an ElGamal commitment for the money created in a Bitcoin block, while providing Pedersen commitments as in the current design of CT for the individual transactions? This means that a quantum or ECDLP breaking attacker can "snoop" into the overall value created in a block, but this should either be already known or uninteresting, while although he could in theory violate the binding property of individual transactions, this would in turn violate the binding of the block-level commitment which is supposed to be impossible?

I suspect my original line of thinking is somehow incoherent (how, mathematically, are the block-level and transaction-level commitments related?), but Dr Back seems to have in mind something involving coinjoin-like interactivity. I am leaving it here without attempting to describe further, because the question seems to continue to be interesting and if there is a solution (even perhaps if it involves interactivity), it would be a hugely important fact, making CT a much more plausible technology for a global money.

Build the wall?

We could also take the Trumpian approach - it's far from infeasible to imagine that there is a mechanism that prevents CT coins arriving back into plaintext area without allowing any hidden inflation that might occur to "infect". This is essentially the sidechain model, except it could be implemented in a variety of different ways. In fact, this model already does exist in the sidechain Liquid, which uses CT. But there have also been proposals to implement CT as a kind of extension block (which has slightly different tradeoffs to a sidechain), for example see ZmnSCPxj's note here