Three-Input Ciphertext Multiplication for Homomorphic Encryption

• URL: http://arxiv.org/abs/2410.13545v1
• Date: Thu, 17 Oct 2024 13:40:49 GMT
• Title: Three-Input Ciphertext Multiplication for Homomorphic Encryption
• Authors: Sajjad Akherati, Yok Jye Tang, Xinmiao Zhang,
• Abstract summary: 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.
• Score: 6.390468088226496
• License:
• Abstract: Homomorphic encryption (HE) allows computations to be directly carried out on ciphertexts and is essential to privacy-preserving computing, such as neural network inference, medical diagnosis, and financial data analysis. Only addition and 2-input multiplication are defined over ciphertexts in popular HE schemes. However, many HE applications involve non-linear functions and they need to be approximated using high-order polynomials to maintain precision. To reduce the complexity of these computations, this paper proposes 3-input ciphertext multiplication. One extra evaluation key is introduced to carry out the relinearization step of ciphertext multiplication, and new formulas are proposed to combine computations and share intermediate results. Compared to using two consecutive 2- input multiplications, computing the product of three ciphertexts utilizing the proposed scheme leads to almost a half of the latency, 29% smaller silicon area, and lower noise without scarifying the throughput.

Related papers

• Encrypted system identification as-a-service via reliable encrypted matrix inversion [0.0]
  Encrypted computation opens up promising avenues across a plethora of application domains. In particular, Arithmetic homomorphic encryption is a natural fit for cloud-based computational services. This paper presents an encrypted system identification service enabled by a reliable encrypted solution to at least squares problems.
  arXiv  Detail & Related papers  (2024-10-27T20:00:04Z)
• 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)
• Implementation of Entropically Secure Encryption: Securing Personal Health Data [0.704590071265998]
  Entropically Secure Encryption (ESE) offers unconditional security with shorter keys to the One-Time Pad. We present the first implementation of ESE for bulk encryption.
  arXiv  Detail & Related papers  (2024-04-04T12:07:33Z)
• Bit-flipping Decoder Failure Rate Estimation for (v,w)-regular Codes [84.0257274213152]
  We propose a new technique to provide accurate estimates of the DFR of a two-iterations (parallel) bit flipping decoder. We validate our results, providing comparisons of the modeled and simulated weight of the syndrome, incorrectly-guessed error bit distribution at the end of the first iteration, and two-itcrypteration Decoding Failure Rates (DFR)
  arXiv  Detail & Related papers  (2024-01-30T11:40:24Z)
• A new lightweight additive homomorphic encryption algorithm [0.0]
  This article describes a lightweight additive homomorphic algorithm with the same encryption and decryption keys. It reduces the computational cost of encryption and decryption from modular exponentiation to modular multiplication.
  arXiv  Detail & Related papers  (2023-12-12T05:12:20Z)
• 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)
• Stealing the Decoding Algorithms of Language Models [56.369946232765656]
  A key component of generating text from modern language models (LM) is the selection and tuning of decoding algorithms. In this work, we show, for the first time, that an adversary with typical API access to an LM can steal the type and hyper parameters of its decoding algorithms. Our attack is effective against popular LMs used in text generation APIs, including GPT-2, GPT-3 and GPT-Neo.
  arXiv  Detail & Related papers  (2023-03-08T17:15:58Z)
• 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)
• FFConv: Fast Factorized Neural Network Inference on Encrypted Data [9.868787266501036]
  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.
  arXiv  Detail & Related papers  (2021-02-06T03:10:13Z)
• New Oracle-Efficient Algorithms for Private Synthetic Data Release [52.33506193761153]
  We present three new algorithms for constructing differentially private synthetic data. The algorithms satisfy differential privacy even in the worst case. Compared to the state-of-the-art method High-Dimensional Matrix Mechanism citeMcKennaMHM18, our algorithms provide better accuracy in the large workload.
  arXiv  Detail & Related papers  (2020-07-10T15:46:05Z)
• 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)

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.