# Towards Fast and Scalable Private Inference

arXiv: https://arxiv.org/abs/2307.04077 · HTML: https://mlsanity.com/paper/2307.04077/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 9 Jul 2023.

## Builds on (most cited first, 17 of 17)

- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning](https://mlsanity.com/paper/2004.02229/) (2020)
- [CrypTFlow: Secure TensorFlow Inference](https://mlsanity.com/paper/1909.07814/) (2019)
- [DeepReDuce: ReLU Reduction for Fast Private Inference](https://mlsanity.com/paper/2103.01396/) (2021)
- [CryptoNAS: Private Inference on a ReLU Budget](https://mlsanity.com/paper/2006.08733/) (2020)
- [Selective Network Linearization for Efficient Private Inference](https://mlsanity.com/paper/2202.02340/) (2022)
- [F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version)](https://mlsanity.com/paper/2109.05371/) (2021)
- [Circa: Stochastic ReLUs for Private Deep Learning](https://mlsanity.com/paper/2106.08475/) (2021)
- [Sphynx: ReLU-Efficient Network Design for Private Inference](https://mlsanity.com/paper/2106.11755/) (2021)
- [Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning](https://mlsanity.com/paper/2107.12342/) (2021)
- [AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference](https://mlsanity.com/paper/2201.06699/) (2022)
- [BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption](https://mlsanity.com/paper/2112.15479/) (2021)
- [ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse](https://mlsanity.com/paper/2205.00922/) (2022)
- [Characterizing and Optimizing End-to-End Systems for Private Inference](https://mlsanity.com/paper/2207.07177/) (2022)
- [HAAC: A Hardware-Software Co-Design to Accelerate Garbled Circuits](https://mlsanity.com/paper/2211.13324/) (2022)
- [Homomorphically Encrypted Computation using Stochastic Encodings](https://mlsanity.com/paper/2203.02547/) (2022)
- [RPU: The Ring Processing Unit](https://mlsanity.com/paper/2303.17118/) (2023)

## Built on by (most cited first, 1 of 1)

- [PriViT: Vision Transformers for Fast Private Inference](https://mlsanity.com/paper/2310.04604/) (2023)

## Abstract (arXiv)

Privacy and security have rapidly emerged as first order design constraints. Users now demand more protection over who can see their data (confidentiality) as well as how it is used (control). Here, existing cryptographic techniques for security fall short: they secure data when stored or communicated but must decrypt it for computation. Fortunately, a new paradigm of computing exists, which we refer to as privacy-preserving computation (PPC). Emerging PPC technologies can be leveraged for secure outsourced computation or to enable two parties to compute without revealing either users' secret data. Despite their phenomenal potential to revolutionize user protection in the digital age, the realization has been limited due to exorbitant computational, communication, and storage overheads. This paper reviews recent efforts on addressing various PPC overheads using private inference (PI) in neural network as a motivating application. First, the problem and various technologies, including homomorphic encryption (HE), secret sharing (SS), garbled circuits (GCs), and oblivious transfer (OT), are introduced. Next, a characterization of their overheads when used to implement PI is covered. The characterization motivates the need for both GCs and HE accelerators. Then two solutions are presented: HAAC for accelerating GCs and RPU for accelerating HE. To conclude, results and effects are shown with a discussion on what future work is needed to overcome the remaining overheads of PI.