Secure Outsourcing of Matrix Determinant Computation under the Malicious Cloud

Article Computer Science, Information Systems

Cloud-Based Outsourcing for Enabling Privacy-Preserving Large-Scale Non-Negative Matrix Factorization

Anmin Fu et al.

Summary: This paper presents a novel outsourced scheme for Non-negative Matrix Factorization (O-NMF) to alleviate clients' computing burden and address data privacy and verification issues when outsourcing NMF.

IEEE TRANSACTIONS ON SERVICES COMPUTING (2022)

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Article Computer Science, Information Systems

Secure and Verifiable Outsourcing of Large-Scale Nonnegative Matrix Factorization (NMF)

Jia Duan et al.

Summary: This work addresses the issue of secure outsourcing large-scale nonnegative matrix factorization (NMF) to the cloud by proposing a scheme that allows clients to verify results with small overhead. The protection of input matrix is achieved through a random permutation and scaling encryption mechanism, and a single-round verification strategy is proposed based on the iterative nature of NMF computation. Theoretical and experimental results demonstrate the superior performance of the proposed scheme.

IEEE TRANSACTIONS ON SERVICES COMPUTING (2021)

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Article Chemistry, Analytical

On the Security and Privacy Challenges of Virtual Assistants

Tom Bolton et al.

Summary: Since the introduction of Siri by Apple, virtual assistants have become increasingly popular. Concerns over security and privacy issues have motivated research in this area, highlighting the need for advancements in voice authentication and compliance with privacy regulations.

SENSORS (2021)

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Article Computer Science, Information Systems

Blockchain-Based Secure Outsourcing of Polynomial Multiplication and Its Application in Fully Homomorphic Encryption

Mingyang Song et al.

Summary: This paper presents a generic blockchain-based framework for secure computation outsourcing and proposes an algorithm for secure outsourcing of polynomial multiplication, reducing local computation cost to O(n). By integrating the algorithm into fully homomorphic encryption, a more efficient outsourcing scheme is achieved. Security analysis demonstrates privacy protection against passive attackers and cheating detection against active attackers.

SECURITY AND COMMUNICATION NETWORKS (2021)

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Article Computer Science, Information Systems

Secure Outsourced Computation of Matrix Determinant Based on Fully Homomorphic Encryption

Haoran Zong et al.

Summary: This paper investigates secure outsourced computation of matrix determinant based on fully homomorphic encryption and proposes an efficient secure outsourced computation scheme. Experimental results show that the scheme efficiently computes the matrix determinant and can be easily applied as a submodule in high-level applications.

IEEE ACCESS (2021)

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Article Computer Science, Theory & Methods