Introducing Sonic: A Practical zk-SNARK with a Nearly Trustless Setup

In this post, we discuss a new zk-SNARK, Sonic, developed by Mary Maller, Sean Bowe, Markulf Kohlweiss and Sarah Meiklejohn. Unlike other SNARKs, Sonic does not require a trusted setup for each circuit, but only a single setup for all circuits. Further, the setup for Sonic never has to end, so it can be continuously secured by accumulating more contributions. This property makes it ideal for any system where there is not a trusted party, and there is a need to validate data without leaking confidential information. For example, a company might wish to show solvency to an auditor without revealing which products they have invested in. The construction is highly practical.

More about zk-SNARKs

Like all other zero-knowledge proofs, zk-SNARKs are a tool used to build applications where users must prove the validity of their data, such as in verifiable computation or anonymous credentials. Additionally, zk-SNARKs have the smallest proof sizes and verifier time out of all other known techniques for building zero-knowledge proofs. However, they typically require a trusted setup process, introducing the possibility of fraudulent data being input by the actors that implemented the system. For example, Zcash uses zk-SNARKs to send private cryptocurrency transactions, and if their setup was compromised then a small number of users could generate an unlimited supply of currency without detection.

Characteristics of zk-SNARKs
🙂 Can be used to build many cryptographic protocols
🙂 Very small proof sizes
🙂 Very fast verifier time
😐 Average prover time
☹️ Requires a trusted setup
☹️ Security assumes non-standard cryptographic assumptions

In 2018, Groth et al. introduced a zk-SNARK that could be built from an updatable and universal setup. We describe these properties below and claim that these properties help mitigate the security concerns around trusted setup. However, unlike Sonic, Groth et al.’s setup outputs a large set of global parameters (in the order of terabytes), which would be unwieldy to store, update and verify.

Updatability

Updatability means that any user, at any time, can update the parameters, including after the system goes live. After a single honest user has participated, no party can prove fraudulent data. This property means that a distrustful user could update the parameters themselves and have personal confidence in the parameters from that point forward. The update proofs are short and quick to verify.

Universality

Universality means that the same parameters can be used for any application using this zk-SNARK. Thus one can imagine including the global parameters in an open-source implementation, or one could use the same parameters for all smart contracts in Ethereum.

Why Use Sonic?

Sonic is universal, updatable, and has a small set of global parameters (in the order of megabytes). Proof sizes are small (256 bytes) and verifier time is competitive with the fastest zk-SNARKs in the literature. It is especially well suited to systems where the same zk-SNARK is run by many different provers and verified by many different parties. This is exactly the situation for many blockchain systems.

Continue reading Introducing Sonic: A Practical zk-SNARK with a Nearly Trustless Setup

An untapped resource to reproduce studies

Science is generally accepted to operate by conducting specially-designed structured observations (such as experiments and case studies) and then interpreting the results to build generalised knowledge (sometimes called theories or models). An important, nay necessary, feature of the social operation of science is transparency in the design, conduct, and interpretation of these structured observations. We’re going to work from the view that security research is science just like any other, though of course as its own discipline it has its own tools, topics, and challenges. This means that studies in security should be replicable, reproducible, or at least able to be corroborated. Spring and Hatleback argue that transparency is just as important for computer science as it is for experimental biology. Rossow et al. also persuasively argue that transparency is a key feature for malware research in particular. But how can we judge whether a paper is transparent enough? The natural answer would seem to be if it is possible to make a replication attempt from the materials and information in the paper. Forget how often the replications succeed for now, although we know that there are publication biases and other factors that mess with that.

So how many security papers published in major conferences contain enough information to attempt a reproduction? In short, we don’t know. From anecdotal evidence, Jono and a couple students looked through the IEEE S&P 2012 proceedings in 2013, and the results were pretty grim. But heroic effort from a few interested parties is not a sustainable answer to this question. We’re here to propose a slightly more robust solution. Master’s students in security should attempt to reproduce published papers as their capstone thesis work. This has several benefits, and several challenges. In the following we hope to convince you that the challenges can be mitigated and the benefits are worth it.

This should be a choice, but one that master’s students should want to make. If anyone has a great new idea to pursue, they should be encouraged to do so. However, here in the UK, the dissertation process is compressed into the summer and there’s not always time to prototype and pilot study designs. Selecting a paper to reproduce, with a documented methodology in place, lets the student get to work faster. There is still a start-up cost; students will likely have to read several abstracts to shortlist a few workable papers, and then read these few papers in detail to select a good candidate. But learning to read, shortlist, and study academic papers is an important skill that all master’s students should be attempting to, well, master. This style of project would provide them with an opportunity to practice these skills.

Briefly, let’s be clear what we mean by reproduction of published work.
Reproduction isn’t just one thing. There’s reproduce and replicate and corroborate and controlled variation (see Feitelson for details). Not everything is amenable to reproduction. For example, case studies (such as attack papers) or natural experiments are often interesting because they are unique. Corroborating some aspect of the case may be possible with a new study, and such study is also valuable. But this not the sort of reproduction we have in mind to advocate here.

Continue reading An untapped resource to reproduce studies