Hey! I'm David, cofounder of zkSecurity and the author of the Real-World Cryptography book. I was previously a crypto architect at O(1) Labs (working on the Mina cryptocurrency), before that I was the security lead for Diem (formerly Libra) at Novi (Facebook), and a security consultant for the Cryptography Services of NCC Group. This is my blog about cryptography and security and other related topics that I find interesting.
I guess I don't post that much about the startup I cofounded more than a year ago, so this is a good opportunity to release a short note for the curious people who read this blog!
I posted a retrospect on the main blog of zkSecurity: A Year of ZK Security, but more time has passed since and here's how things are looking like.
We've had a good stream of clients, and we are now much more financially stable. We've managed to ramp up the team so that we stop losing work opportunities due to lack of availability on our side (we're now 15 engineers, interns included). Not only is the founding team quite the dream team, but the team we created are made of people more qualified than me, so we have a good thing going on.
Everybody seems to have quite a different background, some people are more focused on research, others are stronger devs, and others are CTFs people wearing the security hat. So much so that our differing interests have led us to expand to more than just auditing ZK. We now do development, formal verification work, and design/research work as well. We also are not solely looking at ZK anymore, but at advanced cryptography in general. Think consensus protocols, threshold cryptography, MPC, FHE, etc.
Perhaps naming the company "zk"security was a mistake :) but at least we made a name for ourselves in a smaller market, and are now expanding to more markets!
Any bleeding-edge field has trouble agreeing on terms, and only with time some terms end up stabilizing and standardizing themselves. Some of these terms are a bit weird in the zero-knowledge proof field: we talk about SNARKs and SNARGs and zk-SNARKs and STARKs and so on.
It all started from a clever pun "succinct non-interactive argument of knowledge" and ended up with weird consequences as not every new scheme was deemed "succinct". So much so that naming branched (STARKs are "scalable" and not "succinct") or some schemes can't even be called anything. This is mostly because succinct not only means really small proofs, but also really small verifier running time.
If we were to classify verifier running time between the different schemes it usually goes like this:
KZG (used in Groth16 and Plonk): super fast
FRI (used in STARKs): fast
Bulletproof (used in kimchi): somewhat fast
Using the almost-standardized categorization, only the first one can be called a SNARK, the second one is usually called a STARK, and I'm not even sure how we call the third one, a NARK?
But does it really make sense to reserve SNARK to the first scheme? It turns out people are using all three schemes because they are all dope and fast(er) than running the program by yourself. Since SNARK has become the main term for general-purpose zero-knowledge proofs, then let's just use that!
the “right” definition of succinct should capture any protocol with qualitatively interesting verification costs – and by “interesting,” I mean anything with proof size and verifier time that is smaller than the associated costs of the trivial proof system. By “trivial proof system,” I mean the one in which the prover sends the whole witness to the verifier, who checks it directly for correctness
If you didn't know, slashing is the act of punishing malicious validators in BFT consensus protocols by taking away tokens. Often tokens that were deposited and locked by the validators themselves in order to participate in the consensus protocol. Perhaps the first time that this concept of slashing appeared was in Tendermint? But I'm not sure.
In the article they make the point that BFT consensus protocols need slashing, and are less secure without it. This is an interesting claim as there's a number of BFT consensus protocols that are running without slashing (perhaps a majority of them?)
Slashing is mostly applied to safety violations (a fork of the state of the network) that can be proved. This is often done by witnessing two conflicting messages being signed by the same node in the protocol (often called equivocation). Any "forking attack" that want to be successful will require a threshold (usually a third) of the nodes signing conflicting messages.
Slashing only affects nodes that still have stake in the system, meaning that forking old history to target a node that’s catching up (without a checkpoint) doesn’t get affected by slashing (what we call long-range attacks). The post argues that we need to separate the cost-of-corruption from the profit-from-corruption and seem to only assume that the cost-of-corruption is always the full stake of all attackers, in which case this analysis only makes sense in attacks aiming at forking the tip/head of the blockchain.
The post present a new model, the Corruption-Analysis model, in order to analyze slashing. They accompany the model with the following table that showcases the different outcomes of an attack in a protocol that does not have slashing:
Briefly, it shows that (in the bottom-left corner) failed attacks don't really have a cost as attackers keep their stake (S) and potentially get paid (B1) to do the attack. On the other hand it shows that (in the top-right corner) people will likely punish everyone in case of an attack by mass selling and taking the token price down to $0$ (an implied feature they call token toxicity).
On the other hand, this is the table they show once slashing is integrated:
As one can see, the bottom-left corner and the top-right corner have changed: a failed attack now has a cost, and a successful attack almost doesn't have one anymore. Showing that slashing is strictly better than not slashing.
While this analysis is great to read, I'm not sure I fully agree with it. First, where did the "token toxicity" go in the case of a successful attack? I would argue that a successful attack would impact the protocol in similar ways. Perhaps not as intensely, as as soon as the attack is detected the attackers would lose their stake and not be able to perform another attack, but still this would show that the economic security of the network is not good enough and people would most likely lose their trust in the token.
Second, is the case where the attack was not successful really a net improvement? My view is that failed attacks generally happen due to accidents rather than legitimate attempts, as an attacker would most likely succeed if they had enough to perform an attack. And indeed, I believe all of the slashing events we've seen were all accidents, and no successful BFT attacks was ever witnessed (slashing or no slashing). (That being said, there are cases where an attacker might have difficulties properly isolating a victim's node, in which case it might be harder to always be successful at performing the attack. This really depends on the protocol and on the power of the adversary.)
In addition, attacks are all different. The question of who is targeted matters: if the victim reacts, how long does it take them to punish the attackers and publish the equivocation proofs? Does the protocol has a long-enough unstaking period to give the victim time to punish them? And if a third of the network is Byzantine, can they prevent the slashing from happening anyway by censoring transactions or something? Or worst case, can they kill the liveness of the network instead of getting slashed, punishing everyone and not just them (up until a hard fork occurs)?
Food for thought, but this shows that slashing is still quite hard to model. After all, it's a heuristic, and not a mechanism that you will find in any BFT protocol whitepaper. As such, it is part of the overall economic security of the deployed protocol and has to be measured via both its upsides and downsides.
I think a lot about how to express concepts and find good abstractions to communicate about proof systems. With good abstractions it's much easier to explain how proof systems work and how they differ as they then become more or less lego-constructions put up from the same building blocks.
For example, you can sort of make these generalization and explain most modern general-purpose ZKP systems with them:
They all use a polynomial commitment scheme (PCS). Thank humanity for that abstraction. The PCS is the core engine of any proof system as it dictates how to commit to vectors of values, how large the proofs will be, how heavy the work of the verifier will be, etc.
Their constraint systems are basically all acting on execution trace tables, where columns can be seen as registers in a CPU and the rows are the values in these registers at different moments in time. R1CS has a single column, both AIR and plonkish have as many columns as you want.
They all reduce these columns to polynomials, and then use the fact that for some polynomial $f$ that should vanish on some points ${w_0, w_1, \cdots}$ then we have that $f(x) = [(x - w_0)(x-w_1)\cdots] \cdot q(x)$ for some $q(x)$
They also all use the fact that proving that several identities are correct (e.g. $a_i = b_i$ for all $i$) is basically the same as proving that their random linear combination is the same (i.e. $\sum_i r_i (a_i - b_i) = 0$), which allows us to "compress" checks all over the place in these systems.
Knowing these 4-5 points will get you a very long way. For example, you should be able to quickly understand how STARKs work.
Having said that, I more recently noticed another pattern that is used all over the place by all these proof systems, yet is never really abstracted away, the interactive arithmetization pattern (for lack of a better name). Using this concept, you can pretty much see AIR, Plonkish, and a number of protocols in the same way: they're basically constraint systems that are iteratively built using challenges from a verifier.
Thinking about it this way, the difference between STARK's AIR and Plonks' plonkish arithmetization is now that one (Plonk) has fixed columns that can be preprocessed and the other doesn't. The permutation of Plonk is now nothing special, the write-once memory of Cairo is nothing special as well, they're both interactive arithmetizations.
Let's look at plonk as a table, where the left table is the one that is fixed at compilation time, and the right one is the execution trace that is computed at runtime when a prover runs the program it wants to prove:
One can see the permutation argument of plonk as an extra circuit, that requires 1) the first circuit to be ran and 2) a challenge from the verifier, in order to be secure.
As a diagram, it would look like this:
Now, one could see the write-once memory primitive of Cairo in the same way (which I explained here), or the lookup arguments of a number of proof systems in the same way.
For example, the log-derivative lookup argument used in protostar (and in most protocols nowadays) looks like this. Notice that:
in step 4 the execution trace of the main circuit is sent
in step 6 the verifier sends a challenge back
and in step 7 the prover sends the execution trace of the second circuit (that implements a lookup circuit) using the challenge
As such, the point I really want to make is that a number of ZKP primitives and interaction steps can be seen as an interactive process to construct a super constraint system from a number of successive constraint system iterated on top of each other. Where constraint system means "optionally some new fixed columns, some new runtime columns, and some constraints on all the tables including previous tables". That's it.
Perhaps we should call these iterative constraint systems.
In this episode, David Wong introduces Ethereum and explains its account-based model and the concept of smart contracts. He compares Ethereum to Bitcoin and highlights the advantages of Ethereum's more expressive programming capabilities. He also discusses the execution of transactions and the role of the Ethereum network in maintaining the state of smart contracts.
In this episode, David Wong interviews Kevin Hurley and Alex Akselrod to discuss layer 2 solutions on Bitcoin. They explain that layer 2 (L2) is a scalability solution that sits on top of the Bitcoin blockchain and adds additional functionality and programmability. They discuss the history of L2s, mentioning Namecoin and colored coins as early examples. They also explore the distinction between L2s and sidechains, with L2s typically allowing for unilateral exits and sidechains relying on federations or merge mining. They mention projects like Blockstream, Stacks, and Rootstock as examples of L2s or sidechains on Bitcoin. The conversation explores the concept of multi-signature (multi-sig) and its role in layer 2 (L2) solutions like Liquid. It also delves into the workings of the Lightning Network, which enables instant and low-cost transactions on Bitcoin. The Lightning Network operates through payment channels, where users can push 'beads' (Satoshi's) back and forth. The conversation also touches on the challenges of centralization in Lightning Network and the role of LightSpark in making Lightning more accessible and user-friendly.
