Static program analysis tools can automatically prove many useful properties of programs. However, using static analysis to prove to a third party that a program satisfies a property requires revealing the program's source code. We introduce the concept of zero-knowledge static analysis, in which the prover constructs a zero-knowledge proof about the outcome of the static analysis without revealing the program. We present novel zero-knowledge proof schemes for intra- and inter-procedural abstract interpretation. Our schemes are significantly more efficient than the naive translation of the corresponding static analysis algorithms using existing schemes. We evaluate our approach empirically on real and synthetic programs; with a pairing-based zero knowledge proof scheme as the backend, we are able to prove the control flow analysis on a 2,000-line program in 1,738s. The proof is only 128 bytes and the verification time is 1.4ms. With a transparent zero knowledge proof scheme based on discrete-log, we generate the proof for the tainting analysis on a 12,800-line program in 406 seconds, the proof size is 282 kilobytes, and the verification time is 66 seconds.

This paper follows the line of works that design concretely efficient transparent sublinear zero-knowledge Interactive Oracle Proofs (IOP). Arguments obtained via this paradigm have the advantages of not relying on public-key cryptography, not requiring a trusted setup, and resistance to known quantum attacks. In the realm of transparent systems, Ligero and Aurora stand out with incomparable advantages where the former has a fast prover algorithm somewhat succinct proofs and the latter has somewhat fast prover and succinct proofs. In this work, we introduce Ligero++ that combines the best features of both approaches to achieve the best of both worlds. We implement our protocol and benchmark the results.

Machine learning has become increasingly prominent and is widely used in various applications in practice. Despite its great success, the integrity of machine learning predictions and accuracy is a rising concern. The reproducibility of machine learning models that are claimed to achieve high accuracy remains challenging, and the correctness and consistency of machine learning predictions in real products lack any security guarantees. In this paper, we initiate the study of zero knowledge machine learning and propose protocols for zero knowledge decision tree predictions and accuracy tests. The protocols allow the owner of a decision tree model to convince others that the model computes a prediction on a data sample, or achieves a certain accuracy on a public dataset, without leaking any information about the model itself. We develop approaches to efficiently turn decision tree predictions and accuracy into statements of zero knowledge proofs. We implement our protocols and demonstrate their efficiency in practice. For a decision tree model with 23 levels and 1,029 nodes, it only takes 250 seconds to generate a zero knowledge proof proving that the model achieves high accuracy on a dataset of 5,000 samples and 54 attributes, and the proof size is around 287 kilobytes.