Jongeun Lee

Jaewoo Park, Chenghao Quan, Hyungon Moon et al.

Machine learning models are often provisioned as a cloud-based service where the clients send their data to the service provider to obtain the result. This setting is commonplace due to the high value of the models, but it requires the clients to forfeit the privacy that the query data may contain. Homomorphic encryption (HE) is a promising technique to address this adversity. With HE, the service provider can take encrypted data as a query and run the model without decrypting it. The result remains encrypted, and only the client can decrypt it. All these benefits come at the cost of computational cost because HE turns simple floating-point arithmetic into the computation between long (degree over 1024) polynomials. Previous work has proposed to tailor deep neural networks for efficient computation over encrypted data, but already high computational cost is again amplified by HE, hindering performance improvement. In this paper we show hyperdimensional computing can be a rescue for privacy-preserving machine learning over encrypted data. We find that the advantage of hyperdimensional computing in performance is amplified when working with HE. This observation led us to design HE-HDC, a machine-learning inference system that uses hyperdimensional computing with HE. We carefully structure the machine learning service so that the server will perform only the HE-friendly computation. Moreover, we adapt the computation and HE parameters to expedite computation while preserving accuracy and security. Our experimental result based on real measurements shows that HE-HDC outperforms existing systems by 26~3000 times with comparable classification accuracy.

Jaewoo Park, Chenghao Quan, Jongeun Lee

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.

Sangyun Oh, Hye-Jin S. Kim, Jongeun Lee et al.

We introduce Repetition-Reduction network (RRNet) for resource-constrained depth estimation, offering significantly improved efficiency in terms of computation, memory and energy consumption. The proposed method is based on repetition-reduction (RR) blocks. The RR blocks consist of the set of repeated convolutions and the residual connection layer that take place of the pointwise reduction layer with linear connection to the decoder. The RRNet help reduce memory usage and power consumption in the residual connections to the decoder layers. RRNet consumes approximately 3.84 times less energy and 3.06 times less meory and is approaximately 2.21 times faster, without increasing the demand on hardware resource relative to the baseline network (Godard et al, CVPR'17), outperforming current state-of-the-art lightweight architectures such as SqueezeNet, ShuffleNet, MobileNet and PyDNet.