Toward Lossless Homomorphic Encryption for Scientific Computation

• URL: http://arxiv.org/abs/2309.07284v1
• Date: Wed, 13 Sep 2023 20:05:31 GMT
• Title: Toward Lossless Homomorphic Encryption for Scientific Computation
• Authors: Muhammad Jahanzeb Khan, Bo Fang, Dongfang Zhao,
• Abstract summary: The study explores the potential of the CKKS scheme in Super Computing and its implications for data privacy and computational efficiency. The first experiment reveals the promising applicability of CKKS to matrix multiplication, indicating marginal differences in Euclidean distance and near-to-zero mean square error. The second experiment, applied to a wildfire dataset, illustrates the feasibility of using encrypted machine learning models without significant loss in accuracy.
• Score: 4.668228426337449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a comprehensive investigation into encrypted computations using the CKKS (Cheon-Kim-Kim-Song) scheme, with a focus on multi-dimensional vector operations and real-world applications. Through two meticulously designed experiments, the study explores the potential of the CKKS scheme in Super Computing and its implications for data privacy and computational efficiency. The first experiment reveals the promising applicability of CKKS to matrix multiplication, indicating marginal differences in Euclidean distance and near-to-zero mean square error across various matrix sizes. The second experiment, applied to a wildfire dataset, illustrates the feasibility of using encrypted machine learning models without significant loss in accuracy. The insights gleaned from the research set a robust foundation for future innovations, including the potential for GPU acceleration in CKKS computations within TenSEAL. Challenges such as noise budget computation, accuracy loss in multiplication, and the distinct characteristics of arithmetic operations in the context of CKKS are also discussed. The paper serves as a vital step towards understanding the complexities and potentials of encrypted computations, with broad implications for secure data processing and privacy preservation in various scientific domains.

Related papers

• Secure numerical simulations using fully homomorphic encryption [2.923600136516929]
  homomorphic encryption (FHE) allows for secure computations on encrypted data. We investigate the viability of using FHE for privacy-preserving numerical simulations of partial differential equations.
  arXiv  Detail & Related papers  (2024-10-29T07:47:10Z)
• 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)
• Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing [93.83016310295804]
  AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for computer vision tasks. In this work, we explore the potential of using this information for probabilistic balanced k-means clustering. Instead of discarding non-optimal solutions, we propose to use them to compute calibrated posterior probabilities with little additional compute cost. This allows us to identify ambiguous solutions and data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real visual data.
  arXiv  Detail & Related papers  (2023-10-18T17:59:45Z)
• Randomized Polar Codes for Anytime Distributed Machine Learning [66.46612460837147]
  We present a novel distributed computing framework that is robust to slow compute nodes, and is capable of both approximate and exact computation of linear operations. We propose a sequential decoding algorithm designed to handle real valued data while maintaining low computational complexity for recovery. We demonstrate the potential applications of this framework in various contexts, such as large-scale matrix multiplication and black-box optimization.
  arXiv  Detail & Related papers  (2023-09-01T18:02:04Z)
• Multivariate Systemic Risk Measures and Computation by Deep Learning Algorithms [63.03966552670014]
  We discuss the key related theoretical aspects, with a particular focus on the fairness properties of primal optima and associated risk allocations. The algorithms we provide allow for learning primals, optima for the dual representation and corresponding fair risk allocations.
  arXiv  Detail & Related papers  (2023-02-02T22:16:49Z)
• Verifiable Encodings for Secure Homomorphic Analytics [10.402772462535884]
  Homomorphic encryption is a promising solution for protecting privacy of cloud-delegated computations on sensitive data. We propose two error detection encodings and build authenticators that enable practical client-verification of cloud-based homomorphic computations. We implement our solution in VERITAS, a ready-to-use system for verification of outsourced computations executed over encrypted data.
  arXiv  Detail & Related papers  (2022-07-28T13:22:21Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Efficient CNN Building Blocks for Encrypted Data [6.955451042536852]
  Homomorphic Encryption (FHE) is a promising technique to enable machine learning and inferencing. We show that operational parameters of the chosen FHE scheme have a major impact on the design of the machine learning model. Our empirical study shows that choice of aforementioned design parameters result in significant trade-offs between accuracy, security level, and computational time.
  arXiv  Detail & Related papers  (2021-01-30T21:47:23Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Cryptotree: fast and accurate predictions on encrypted structured data [0.0]
  Homomorphic Encryption (HE) is acknowledged for its ability to allow computation on encrypted data, where both the input and output are encrypted. We propose Cryptotree, a framework that enables the use of Random Forests (RF), a very powerful learning procedure compared to linear regression.
  arXiv  Detail & Related papers  (2020-06-15T11:48:01Z)
• New advances in enumerative biclustering algorithms with online partitioning [80.22629846165306]
  This paper further extends RIn-Close_CVC, a biclustering algorithm capable of performing an efficient, complete, correct and non-redundant enumeration of maximal biclusters with constant values on columns in numerical datasets. The improved algorithm is called RIn-Close_CVC3, keeps those attractive properties of RIn-Close_CVC, and is characterized by: a drastic reduction in memory usage; a consistent gain in runtime.
  arXiv  Detail & Related papers  (2020-03-07T14:54:26Z)

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.