# Efficient Skip Connections Realization for Secure Inference on Encrypted Data

arXiv: https://arxiv.org/abs/2306.06736 · HTML: https://mlsanity.com/paper/2306.06736/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 11 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [The Shattered Gradients Problem: If resnets are the answer, then what is the question?](https://mlsanity.com/paper/1702.08591/) (2017)
- [DiracNets: Training Very Deep Neural Networks Without Skip-Connections](https://mlsanity.com/paper/1706.00388/) (2017)
- [HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data](https://mlsanity.com/paper/2011.01805/) (2020)

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

- [Power-Softmax: Towards Secure LLM Inference over Encrypted Data](https://mlsanity.com/paper/2410.09457/) (2024)

## Abstract (arXiv)

Homomorphic Encryption (HE) is a cryptographic tool that allows performing computation under encryption, which is used by many privacy-preserving machine learning solutions, for example, to perform secure classification. Modern deep learning applications yield good performance for example in image processing tasks benchmarks by including many skip connections. The latter appears to be very costly when attempting to execute model inference under HE. In this paper, we show that by replacing (mid-term) skip connections with (short-term) Dirac parameterization and (long-term) shared-source skip connection we were able to reduce the skip connections burden for HE-based solutions, achieving x1.3 computing power improvement for the same accuracy.