In the Machine Learning as a Service paradigm, a service provider (e.g., a server) hosting a model offers inference APIs to clients, who can send their queries and receive the inference results. While most recent secure inference works focus on addressing privacy issues, they overlook the importance of checking the service quality and reliability. A malicious server may deviate from the protocol specification to deliberately provide incorrect services such as using low-quality models. Thus, it is necessary to design new solutions to empower clients to verify the server’s model accuracy and inference integrity while protecting both parties’ privacy. We present Conan, a new secure and reliable inference framework against malicious servers to achieve accuracy verification, inference integrity, and privacy simultaneously. In Conan, the server first commits to the model and proves in zero-knowledge that the committed model achieves the claimed accuracy. Then both parties perform secure inference on the committed model against the malicious server. To instantiate the above framework, we design generic maliciously secure two-party computation (2PC) protocols with a fixed corrupted party, which may be of independent interest. Our protocols achieve high efficiency by utilizing the advantage that the semi-honest party can check the behavior of the corrupted party. Furthermore, they support both arithmetic and Boolean circuit evaluation, a crucial attribute for secure inference on complicated machine learning models. We implement the fixed-corruption 2PC protocols for our secure and reliable inference. The experimental results show 1 ~ 2 orders of magnitude improvements over conventional maliciously secure protocols in terms of communication and computation costs.

Deep learning models have achieved unprecedented success in various domains, such as healthcare and finance. However, deploying model inference in real-world applications, where data is distributed among multiple entities, poses significant privacy concerns. Existing secure model inference work has limitations in computational overhead and scalability, especially when dealing with complex models and multiple parties with vertically partitioned data. In this work, we design and implement an efficient and scalable secure inference framework for vertically partitioned data, supporting execution with a large number of parties. Our work considers a semi-honest setting with all-but-one corruptions. The core of our framework is a series of secure and efficient protocols for complex non-linear functions of the model inference, such as ReLU and Maxpool. These protocols are designed based on secure multi-party computation preliminaries, significantly enhancing efficiency while maintaining rigorous security guarantees. We conduct comprehensive experiments to evaluate the performance of our framework. Experimental results show that SecInfer substantially improves the communication and computation performance of secure naive inference works by up to 3.71 × and 3.42 ×, respectively.

Trigonometric functions are crucial non-linear operations used in scientific computation and complex machine learning models. However, existing secure computing frameworks either lack support for these operations, or suffer from undesirable performance bottleneck. In this paper, we present an efficient and precise fixed-point framework called TriFSS for securely evaluating trigonometric functions. Specifically, we first design new building blocks based on advanced Function Secret Sharing techniques, achieving reduced communication and computation overhead. Second, with these efficient components, we propose a general evaluation process for these functions, in which periodic properties are fully exploited for better performance. Moreover, we implement the TriFSS framework and conduct extensive experiments. The experimental results show that our protocols achieve at least 23x less communication overhead and 2.8x less latency than the state-of-the-art frameworks, while only resulting in 1 ULP error, which is comparable to floating-point based works.

Private neural network inference has demonstrated great importance in various privacy-critical scenarios. However, the primary challenge remaining in prior works is that the evaluation on encrypted data levies prohibitively high run-time and communication overhead. In this work, we present FastSecNet, an efficient two-party cryptographic framework for private inference in the dealer-based pre-processing setting. Specifically, (1) FastSecNet provides an efficient ReLU protocol for the evalution of non-linear layers, which is built up on a recent advanced cryptographic primitive, function secret sharing (FSS). The core of this construction are an optimized ReLU representation and a customized FSS-based ReLU protocol. (2) For linear layer evaluation, we first propose an efficient PRG-based preprocessing protocol based on the fact that one of the inputs is uniformly random in the offline phase. Then, the online phase only communicates one element and consists of lightweight secret-sharing operations in a ring. Extensive evaluations conducted on 4 real-world datasets and 9 neural network models demonstrate that during the online phase, FastSecNet achieves 14× less runtime and 18× less communication cost compared to the state-of-the-art.

We initiate the study of private inference on Transformer-based models in the client-server setting, where clients have private inputs and servers hold proprietary models. Our main contribution is to provide several new secure protocols for matrix multiplication and complex non-linear functions like Softmax, GELU activations, and LayerNorm, which are critical components of Transformers. Specifically, we first propose a customized homomorphic encryption-based protocol for matrix multiplication that crucially relies on a novel compact packing technique. This design achieves $\sqrt{m} \times$ less communication ($m$ is the number of rows of the output matrix) over the most efficient work. Second, we design efficient protocols for three non-linear functions via integrating advanced underlying protocols and specialized optimizations. Compared to the state-of-the-art protocols, our recipes reduce about half of the communication and computation overhead. Furthermore, all protocols are numerically precise, which preserve the model accuracy of plaintext. These techniques together allow us to implement \Name, an efficient Transformer-based private inference framework. Experiments conducted on several real-world datasets and models demonstrate that \Name achieves $3 \sim 14\times$ less communication and $3 \sim 11\times$ less runtime compared to the prior art.