FFConv: Fast Factorized Neural Network Inference on Encrypted Data

• URL: http://arxiv.org/abs/2102.03494v1
• Date: Sat, 6 Feb 2021 03:10:13 GMT
• Title: FFConv: Fast Factorized Neural Network Inference on Encrypted Data
• Authors: Yuxiao Lu, Jie Lin, Chao Jin, Zhe Wang, Khin Mi Mi Aung, Xiaoli Li
• Abstract summary: We propose a low-rank factorization method called FFConv to unify convolution and ciphertext packing. Compared to prior art LoLa and Falcon, our method reduces the inference latency by up to 87% and 12%, respectively.
• Score: 9.868787266501036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Homomorphic Encryption (HE), allowing computations on encrypted data (ciphertext) without decrypting it first, enables secure but prohibitively slow Neural Network (HENN) inference for privacy-preserving applications in clouds. To reduce HENN inference latency, one approach is to pack multiple messages into a single ciphertext in order to reduce the number of ciphertexts and support massive parallelism of Homomorphic Multiply-Add (HMA) operations between ciphertexts. However, different ciphertext packing schemes have to be designed for different convolution layers and each of them introduces overheads that are far more expensive than HMA operations. In this paper, we propose a low-rank factorization method called FFConv to unify convolution and ciphertext packing. To our knowledge, FFConv is the first work that is capable of accelerating the overheads induced by different ciphertext packing schemes simultaneously, without incurring a significant increase in noise budget. Compared to prior art LoLa and Falcon, our method reduces the inference latency by up to 87% and 12%, respectively, with comparable accuracy on MNIST and CIFAR-10.

Related papers

• Transformers -- Messages in Disguise [3.74142789780782]
  NN based cryptography is being investigated due to its ability to learn and implement random cryptographic schemes. R is comprised of three new NN layers: the (i) projection layer, (ii) inverse projection layer, and (iii) dot-product layer. This results in an ANC network that (i) is computationally efficient, (ii) ensures the encrypted message is unique, and (iii) does not induce any communication overhead.
  arXiv  Detail & Related papers  (2024-11-15T15:42:29Z)
• Three-Input Ciphertext Multiplication for Homomorphic Encryption [6.390468088226496]
  Homomorphic encryption (HE) allows computations directly on ciphertexts. HE is essential to privacy-preserving computing, such as neural network inference, medical diagnosis, and financial data analysis. This paper proposes 3-input ciphertext multiplication to reduce complexity of computations.
  arXiv  Detail & Related papers  (2024-10-17T13:40:49Z)
• Efficient Homomorphically Encrypted Convolutional Neural Network Without Rotation [6.03124479597323]
  This paper proposes a novel reformulated joint procedure and a new filter coefficient packing scheme to eliminate ciphertext rotations without affecting the security of the HE scheme. For various plain-20s over the CIFAR-10/100 datasets, our design reduces the running time of the Conv and FC layers by 15.5% and the communication cost between client and server by more than 50%, compared to the best prior design.
  arXiv  Detail & Related papers  (2024-09-08T19:46:25Z)
• Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration [54.897493351694195]
  We propose a novel parallel decoding approach, namely textithidden transfer, which decodes multiple successive tokens simultaneously in a single forward pass. In terms of acceleration metrics, we outperform all the single-model acceleration techniques, including Medusa and Self-Speculative decoding.
  arXiv  Detail & Related papers  (2024-04-18T09:17:06Z)
• Coding-Based Hybrid Post-Quantum Cryptosystem for Non-Uniform Information [53.85237314348328]
  We introduce for non-uniform messages a novel hybrid universal network coding cryptosystem (NU-HUNCC) We show that NU-HUNCC is information-theoretic individually secured against an eavesdropper with access to any subset of the links.
  arXiv  Detail & Related papers  (2024-02-13T12:12:39Z)
• SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
  We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
  arXiv  Detail & Related papers  (2023-09-27T05:19:32Z)
• Encrypted Dynamic Control exploiting Limited Number of Multiplications and a Method using RLWE-based Cryptosystem [0.3749861135832073]
  We present a method to encrypt dynamic controllers that can be implemented through most homomorphic encryption schemes. As a result, the encrypted controller involves only a limited number of homomorphic multiplications on every encrypted data. We propose a customization of the method for Ring Learning With Errors (RLWE)-based cryptosystems, where a vector of messages can be encrypted into a single ciphertext.
  arXiv  Detail & Related papers  (2023-07-07T08:24:48Z)
• 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)
• Recovering AES Keys with a Deep Cold Boot Attack [91.22679787578438]
  Cold boot attacks inspect the corrupted random access memory soon after the power has been shut down. In this work, we combine a novel cryptographic variant of a deep error correcting code technique with a modified SAT solver scheme to apply the attack on AES keys. Our results show that our methods outperform the state of the art attack methods by a very large margin.
  arXiv  Detail & Related papers  (2021-06-09T07:57:01Z)
• Faster Secure Data Mining via Distributed Homomorphic Encryption [108.77460689459247]
  Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field. We propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem. We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets.
  arXiv  Detail & Related papers  (2020-06-17T18:14:30Z)
• TEDL: A Text Encryption Method Based on Deep Learning [10.428079716944463]
  This paper proposes a novel text encryption method based on deep learning called TEDL. Results of experiments and relevant analyses show that TEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution.
  arXiv  Detail & Related papers  (2020-03-09T11:04:36Z)

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.