CipherFormer: Efficient Transformer Private Inference with Low Round Complexity

• URL: http://arxiv.org/abs/2403.16860v1
• Date: Mon, 25 Mar 2024 15:24:57 GMT
• Title: CipherFormer: Efficient Transformer Private Inference with Low Round Complexity
• Authors: Weize Wang, Yi Kuang,
• Abstract summary: We propose CipherFormer, a novel transformer private inference scheme using homomorphic encryption and garbled circuits. In comparison with an advanced homomorphic encryption scheme on text classification tasks, our model improves accuracy by 3% to 11% while performing private inference with a 7.7x-11.9x speedup.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is a growing trend to outsource the inference task of large transformer models to cloud servers. However, this poses a severe threat to users' private data as they are exposed to cloud servers after uploading. Although several works attempted to provide private inference for transformer models, their hundreds of communication rounds limit the application scenarios. Motivated by the desire to minimize round complexity, we propose CipherFormer, a novel transformer private inference scheme using homomorphic encryption and garbled circuits. We present a protocol for quickly computing homomorphic matrix multiplications. We then modify the attention mechanism and design the corresponding garbled circuits. Furthermore, we show how to use a lightweight attention mechanism and mixed-bitwidth to reduce the inference latency while maintaining accuracy. In comparison with an advanced homomorphic encryption scheme on text classification tasks, our model improves accuracy by 3% to 11% while performing private inference with a 7.7x-11.9x speedup.

Related papers

• Differential Transformer [99.5117269150629]
  Transformer tends to overallocate attention to irrelevant context. We introduce Diff Transformer, which amplifies attention to relevant context while canceling noise. It offers notable advantages in practical applications, such as long-context modeling, key information retrieval, hallucination mitigation, in-context learning, and reduction of activation outliers.
  arXiv  Detail & Related papers  (2024-10-07T17:57:38Z)
• I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption [5.12893315783096]
  We introduce BlindTuner, a privacy-preserving fine-tuning system that enables transformer training exclusively on homomorphically encrypted data for image classification. Our findings highlight a substantial speed enhancement of 1.5x to 600x over previous work in this domain.
  arXiv  Detail & Related papers  (2024-02-14T10:15:43Z)
• AttMEMO : Accelerating Transformers with Memoization on Big Memory Systems [10.585040856070941]
  We introduce a novel embedding technique to find semantically similar inputs to identify computation similarity. We enable 22% inference-latency reduction on average (up to 68%) with negligible loss in inference accuracy.
  arXiv  Detail & Related papers  (2023-01-23T04:24:26Z)
• ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers [70.76313507550684]
  We propose a content-based sparse attention method, as an alternative to dense self-attention. Specifically, we cluster and then aggregate key and value tokens, as a content-based method of reducing the total token count. The resulting clustered-token sequence retains the semantic diversity of the original signal, but can be processed at a lower computational cost.
  arXiv  Detail & Related papers  (2022-08-28T04:18:27Z)
• THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption [112.02441503951297]
  Privacy-preserving inference of transformer models is on the demand of cloud service users. We introduce $textitTHE-X$, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models.
  arXiv  Detail & Related papers  (2022-06-01T03:49:18Z)
• Sparse is Enough in Scaling Transformers [12.561317511514469]
  Large Transformer models yield impressive results on many tasks, but are expensive to train, or even fine-tune, and so slow at decoding that their use and study becomes out of reach. We propose Scaling Transformers, a family of next generation Transformer models that use sparse layers to scale efficiently and perform unbatched decoding much faster than the standard Transformer.
  arXiv  Detail & Related papers  (2021-11-24T19:53:46Z)
• Finetuning Pretrained Transformers into RNNs [81.72974646901136]
  Transformers have outperformed recurrent neural networks (RNNs) in natural language generation. A linear-complexity recurrent variant has proven well suited for autoregressive generation. This work aims to convert a pretrained transformer into its efficient recurrent counterpart.
  arXiv  Detail & Related papers  (2021-03-24T10:50:43Z)
• Transformers Solve the Limited Receptive Field for Monocular Depth Prediction [82.90445525977904]
  We propose TransDepth, an architecture which benefits from both convolutional neural networks and transformers. This is the first paper which applies transformers into pixel-wise prediction problems involving continuous labels.
  arXiv  Detail & Related papers  (2021-03-22T18:00:13Z)
• The Cascade Transformer: an Application for Efficient Answer Sentence Selection [116.09532365093659]
  We introduce the Cascade Transformer, a technique to adapt transformer-based models into a cascade of rankers. When compared to a state-of-the-art transformer model, our approach reduces computation by 37% with almost no impact on accuracy.
  arXiv  Detail & Related papers  (2020-05-05T23:32:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.