Secure Outsourced Computation of Matrix Determinant Based on Fully Homomorphic Encryption

Zong, Haoran; Huang, Hai; Wang, Shufang

doi:10.1109/access.2021.3056476

Cited by 11 publications

(4 citation statements)

References 40 publications

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“…Fully homomorphic encryption allows a weak client to securely delegate computation-intensive tasks to the cloud platform and thus greatly reduces the local computational overhead. As fully homomorphic encryption provides a general framework to compute any function on cipher texts, a variety of secure outsourced computation applications based on fully homomorphic encryption have been proposed in recent years, ranging from fundamental functions [15].…”

Section: ) Secure Outsourced Computation Of Matrix Determinant Based ...mentioning

confidence: 99%

The Next Frontier of Security: Homomorphic Encryption in Action

Sabnis,

Mitragotri

2024

IJRASET

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Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

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Section: ) Secure Outsourced Computation Of Matrix Determinant Based ...mentioning

confidence: 99%

The Next Frontier of Security: Homomorphic Encryption in Action

Sabnis,

Mitragotri

2024

IJRASET

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“…Schemes that are completely homomorphic BFV are defined over the cyclotomic polynomial of degree m. The ring is used as the plaintext space for the BFV system. A distinguishing feature of the BFV system and its derivatives is that they permit Single Instruction, Multiple Data (SIMD) parallel processing [10].…”

Section: Fig 1 Approach Of the Fhementioning

confidence: 99%

RCAE_BFV: Retrieve Encrypted Images using Convolution AutoEncoder and BFV

2022

IJCCCE

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Content-Based Image Retrieval (CBIR) is an actual application in computer vision, which retrieves similar images from a database. Deep Learning (DL) is essential in many applications, including image retrieval applications. However, encryption techniques are used to protect data privacy because these data are vulnerable to being viewed by unauthorized parties while being transmitted over unsecured channels. This paper includes two parts for images retrieval. In the first part, features of all images of a Canadian Institute for Advanced Research CIFAR-10 dataset were extracted and stored on the Server-side. In the second part, the Brakerski/Fan-Vercauteren (BFV) homomorphic encryption scheme method for encrypting an image sent by the client-side. First, their decryption and image features are extracted depending on the trainer model when they arrive on the server-side. Then an extracted features are compared with stored features using the Cosine Distance method, and then the server encrypts the retrieved images and sends them to the client-side. Deep-learning results on plain images were 97% for classification and 96.7% for retriever images. At the same time, The National Institute of Standards and Technology (NIST ) test was used to check the security of BFV when applied to CIFAR-10 dataset. Index Terms— BFV, Convolution Autoencoder, Content-based image retrieval, Homomorphic encryption.

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“…[29]), significantly faster execution times for the basic operations in the homomorphically encrypted domain can be achieved. Additionally, depending on the used HE scheme, it might be theoretically possible to incorporate acceleration of linear algebra operations in the encrypted domain [107], [108].…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protection

Drozdowski¹,

Stockhardt²,

Rathgeb³

et al. 2021

Preprint

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Computationally efficient, accurate, and privacypreserving data storage and retrieval are among the key challenges faced by practical deployments of biometric identification systems worldwide.In this work, a method of protected indexing of biometric data is presented. By utilising feature-level fusion of intelligently paired templates, a multi-stage search structure is created. During retrieval, the list of potential candidate identities is successively pre-filtered, thereby reducing the number of template comparisons necessary for a biometric identification transaction. Protection of the biometric probe templates, as well as the stored reference templates and the created index is carried out using homomorphic encryption.The proposed method is extensively evaluated in closedset and open-set identification scenarios on publicly available databases using two state-of-the-art open-source face recognition systems. With respect to a typical baseline algorithm utilising an exhaustive search-based retrieval algorithm, the proposed method enables a reduction of the computational workload associated with a biometric identification transaction by 90%, while simultaneously suffering no degradation of the biometric performance. Furthermore, by facilitating a seamless integration of template protection with open-source homomorphic encryption libraries, the proposed method guarantees unlinkability, irreversibility, and renewability of the protected biometric data.

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Secure Outsourced Computation of Matrix Determinant Based on Fully Homomorphic Encryption

Cited by 11 publications

References 40 publications

The Next Frontier of Security: Homomorphic Encryption in Action

The Next Frontier of Security: Homomorphic Encryption in Action

RCAE_BFV: Retrieve Encrypted Images using Convolution AutoEncoder and BFV

Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protection

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