While homomorphic encryption (HE) provides strong privacy protection, its high computational cost has restricted its application to simple tasks. Recently, hyperdimensional computing (HDC) applied to HE has shown promising performance for privacy-preserving machine learning (PPML). However, when applied to more realistic scenarios such as batch inference, the HDC-based HE has still very high compute time as well as high encryption and data transmission overheads. To address this problem, we propose HDC with encrypted parameters (EP-HDC), which is a novel PPML approach featuring client-side HE, i.e., inference is performed on a client using a homomorphically encrypted model. Our EP-HDC can effectively mitigate the encryption and data transmission overhead, as well as providing high scalability with many clients while providing strong protection for user data and model parameters. In addition to application examples for our client-side PPML, we also present design space exploration involving quantization, architecture, and HE-related parameters. Our experimental results using the BFV scheme and the Face/Emotion datasets demonstrate that our method can improve throughput and latency of batch inference by orders of magnitude over previous PPML methods (36.52~1068x and 6.45~733x, respectively) with less than 1% accuracy degradation.

翻译：尽管同态加密（HE）提供了强大的隐私保护，但其高昂的计算成本限制了其在简单任务中的应用。最近，应用于HE的超维度计算（HDC）在隐私保护机器学习（PPML）方面展现出有前景的性能。然而，当应用于更现实的场景（如批量推理）时，基于HDC的HE仍具有极高的计算时间以及高加密和数据传输开销。为解决这一问题，我们提出了加密参数的超维度计算（EP-HDC），这是一种新颖的PPML方法，采用客户端HE，即在客户端使用同态加密模型进行推理。我们的EP-HDC能有效减轻加密和数据传输开销，同时在保护用户数据和模型参数的同时，为多客户端场景提供高可扩展性。除了我们客户端PPML的应用示例外，我们还展示了涉及量化、架构和HE相关参数的设计空间探索。使用BFV方案和Face/Emotion数据集的实验结果表明，我们的方法在批量推理的吞吐量和延迟方面比先前的PPML方法提升了数个数量级（分别为36.52~1068倍和6.45~733倍），且准确率下降小于1%。