Machine Learning as a service (MLaaS) permits resource-limited clients to access powerful data analytics services ubiquitously. Despite its merits, MLaaS poses significant concerns regarding the integrity of delegated computation and the privacy of the server’s model parameters. To address this issue, Zhang et al. (CCS'20) initiated the study of zero-knowledge Machine Learning (zkML). Few zkML schemes have been proposed afterward; however, they focus on sole ML classification algorithms that may not offer satisfactory accuracy or require large-scale training data and model parameters, which may not be desirable for some applications. We propose ezDPS, a new efficient and zero-knowledge ML inference scheme. Unlike prior works, ezDPS is a zkML pipeline in which the data is processed in multiple stages for high accuracy. Each stage of ezDPS is harnessed with an established ML algorithm that is shown to be effective in various applications, including Discrete Wavelet Transformation, Principal Components Analysis, and Support Vector Machine. We design new gadgets to prove ML operations effectively. We fully implemented ezDPS and assessed its performance on real datasets. Experimental results showed that ezDPS achieves one-to-three orders of magnitude more efficient than the generic circuit-based approach in all metrics while maintaining more desirable accuracy than single ML classification approaches.

Blockchain-based data storage has become an emerging paradigm, providing a fair and transparent data platform for decentralized applications. However, how to achieve secure on-chain verification for arbitrary SQL queries in such a decentralized storage remains under-explored. Due to the limitations of authenticated data structure (ADS), existing works either do not consider arbitrary query verification issue or fail to achieve practical gas consumption efficiency. In this paper, we present a novel arbitrary query verification scheme for decentralized storage. The proposed scheme, named $\mathsf {zkQuery}$ , enables efficient public verification for arbitrary queries with zero-knowledge confidentiality. $\mathsf {zkQuery}$ is built from the ingenious synergy of techniques from both zero-knowledge proof and smart contract technology. The core idea is to delegate smart contracts to fairly execute results verification and utilize our tailored zero-knowledge proof protocol to facilitate arbitrary computation in a privacy-preserving manner. The verification protocols of $\mathsf {zkQuery}$ are highly customized for decentralized storage, where the complexity of on-chain verification can be completed in logarithmic time, significantly decreasing gas consumption. We rigorously provide security analysis and complete the prototype implementation. The extensive experiments over the NEAR blockchain show that $\mathsf {zkQuery}$ can gain at least $2\times $ better performance than the baseline approach on all metrics.

Decentralized storage platforms based on consortium blockchains have emerged in the spotlight of research and industry communities because they are flexible, transparent, and eliminated trust in contrast to the traditional centralized data-sharing model. However, due to wide attacking surfaces in a blockchain network, this decentralized data-sharing paradigm is subject to malicious data breaches. Untrusted blockchain nodes can directly obtain sensitive information from the query processing and their local storage. Several studies have been made for solving this dilemma, but they only focus on single-user settings and cannot be directly applied to multi-owners blockchain-based data sharing scenarios. In this paper, we introduce FedEDB, a federated and encrypted data store by using consortium blockchains. Unlike existing solutions that focus on single-user settings, our proposed schemes can efficiently support privacy-preserving and reliable multi-owner queries in the decentralized setting. We start from the practical key aggregation technique to construct the multi-owner search schemes and further refine the underling building blocks to enhance the security. Besides, we integrate the smart contract with our tailored zero-knowledge proof to enforce secure and reliable result verification protocol with fairness. We implement a prototype and thorough security analysis and comprehensive evaluation results confirm the practicability of our design.

Machine Learning as a Service (MLaaS) offers powerful data analytics services to clients with limited resources. However, it still raises concerns about the integrity of delegated computation and the privacy of the server's model parameters. To address these issues, zero-knowledge Machine Learning (zkML) has been suggested for computation verifiability with privacy guarantee for ML models. Nevertheless, the existing zkML schemes focus on only one classical ML classification algorithm or deep neural networks, which may not achieve satisfactory accuracy or require large-scale training data and model parameters, thus limiting their usefulness in certain applications. In this paper, we propose ezDPS, an efficient and zero-knowledge scheme for classical ML inference that processes data in multiple stages for improved accuracy. Unlike prior works, each stage of the ezDPS pipeline is based on a well-established classical ML algorithm, including Discrete Wavelet Transformation, Zero-Score Normalization, Principal Components Analysis, and Support Vector Machine. We design new gadgets to prove various ML operations effectively. Our implementation of ezDPS has been fully tested on real datasets, and experimental results show that it is up to three orders of magnitude more efficient than generic circuit-based approaches, while also maintaining greater accuracy than single ML classification approaches.