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Abstract We propose a generic compiler that can convert any zero-knowledge (ZK) proof for SIMD circuits to general circuits efficiently, and an extension that can preserve the space complexity of the proof systems. Our compiler can immediately produce new results improving upon state of the art.By plugging in our compiler to Antman, an interactive sublinear-communication protocol, we improve the overall communication complexity for general circuits from$$\mathcal {O}(C^{3/4})$$ $O\left(C^{3/4}\right)$to$$\mathcal {O}(C^{1/2})$$ $O\left(C^{1/2}\right)$. Our implementation shows that for a circuit of size$$2^{27}$$ $2^{27}$, it achieves up to$$83.6\times $$ $83.6\times$improvement on communication compared to the state-of-the-art implementation. Its end-to-end running time is at least$$70\%$$ $70\%$faster in a 10Mbps network.Using the recent results on compressed$$\varSigma $$ $\Sigma$-protocol theory, we obtain a discrete-log-based constant-round zero-knowledge argument with$$\mathcal {O}(C^{1/2})$$ $O\left(C^{1/2}\right)$communication and common random string length, improving over the state of the art that has linear-size common random string and requires heavier computation.We improve the communication of a designatedn-verifier zero-knowledge proof from$$\mathcal {O}(nC/B+n^2B^2)$$ $O(nC/B+n^2{B}^2)$to$$\mathcal {O}(nC/B+n^2)$$ $O(nC/B+n^2)$.To demonstrate the scalability of our compilers, we were able to extract a commit-and-prove SIMD ZK from Ligero and cast it in our framework. We also give one instantiation derived from LegoSNARK, demonstrating that the idea of CP-SNARK also fits in our methodology. 

Individuals and organizations are accumulating data at an unprecedented rate owing to the advent of inexpensive cloud computing. Data owners are increasingly turning to secure and privacy-preserving collaborative analytics to maximize the value of their records. In this paper, we will survey the state-of-the- art of this growing area. We will describe how researchers are bringing security and privacy-enhancing technologies, such as differential privacy, secure multiparty computation, and zero-knowledge proofs, into the query lifecycle. We also touch upon some of the challenges and opportunities associated with deploying these technologies in the field. 

Individuals and organizations are using databases to store personal information at an unprecedented rate. This creates a quandary for data providers. They are responsible for protecting the privacy of individuals described in their database. On the other hand, data providers are sometimes required to provide statistics about their data instead of sharing it wholesale with strong assurances that these answers are correct and complete such as in regulatory filings for the US SEC and other goverment organizations. We introduce a system,ZKSQL, that provides authenticated answers to ad-hoc SQL queries with zero-knowledge proofs. Its proofs show that the answers are correct and sound with respect to the database's contents and they do not divulge any information about its input records. This system constructs proofs over the steps in a query's evaluation and it accelerates this process with authenticated set operations. We validate the efficiency of this approach over a suite of TPC-H queries and our results show that ZKSQL achieves two orders of magnitude speedup over the baseline.