Outsourcing LDA-Based Face Recognition to an Untrusted Cloud

Ren, Yanli; Song, Zhuhuan; Sun, Shi-Feng; Liu, Joseph K.; Feng, Guorui

doi:10.1109/tdsc.2022.3172143

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…To address these concerns, they introduced a series of secure transformations to redesign the encryption, demonstrating that the encrypted matrix is computationally indistinguishable from a random matrix. Ren et al [44] subsequently proposed a secure outsourcing algorithm for face recognition based on Linear Discriminant Analysis (LDA) using Zhang et al's eigendecomposition algorithm [43].…”

Section: A Related Workmentioning

confidence: 99%

Cloud-Assisted Privacy-Preserving Spectral Clustering Algorithm Within a Multi-User Setting

Xu,

Cheng,

Tian

et al. 2024

IEEE Access

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Spectral clustering, a powerful algorithm in the field of AI, holds a significant role despite its inherent high time complexity. For data owners grappling with limitations such as small datasets and restricted computational resources, harnessing the computational capabilities of cloud computing and aggregating data from multiple sources can yield precise spectral clustering results. However, explicit data uploading to cloud servers poses privacy risks. In response to this challenge, we explore the outsourcing dilemma of spectral clustering in a cloud and multi-user environment and propose a quantum-secure and efficient solution. Specifically, by employing the CKKS homomorphic encryption algorithm within a dual non-collusive server model, we formulate a comprehensive and multi-user spectral clustering outsourcing scheme. Our approach addresses privacy concerns by introducing secure computation protocols for L 2 norm, exponential function, and negative half power function. We elaborate on efficient computational algorithms for each stage of spectral clustering, ensuring accurate clustering outcomes without compromising dataset privacy. Moreover, in our scheme, users only need to upload their encrypted dataset without requiring direct interaction with each other or the cloud server until obtaining clustering results. Finally, we argue the IND-CPA security of our design and substantiate its accuracy and efficiency through theoretical comparison analysis and experimental evaluations.

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Section: A Related Workmentioning

confidence: 99%

Cloud-Assisted Privacy-Preserving Spectral Clustering Algorithm Within a Multi-User Setting

Xu,

Cheng,

Tian

et al. 2024

IEEE Access

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“…Probability of a particular hidden sequence is given in (15) that is the product of transition probabilities at different time instances. Similarly, probability of observation sequence for a known hidden sequence is given in ( 16) that is the product of emission probabilities at different time steps.…”

Section: 𝑃(Srmentioning

confidence: 99%

“…Gabor filter extracts texture pattern and edges of faces but it increases redundancy [14]. Linear discriminant analysis (LDA) reduces number of features to a more manageable number prior to classification [15]. Independent component analysis (ICA) extracts the hidden features of image and defines a generative model in FR [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Face Recognition System Based on Four State Hidden Markov Model

et al. 2022

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Computational complexity is a matter of great concern in real time face recognition systems. In this paper, four state hidden Markov model for face recognition has been presented whereby coefficients of feature vectors have been curtailed. Face images have been divided into a sequence of overlapping blocks. An observation sequence containing coefficients of eigen values and eigenvectors of these blocks have been used to train the model and each subject is associated with a separate hidden Markov model. The computational complexity of the proposed model has been minimized by employing discrete wavelet transform in the preprocessing stage. Furthermore, singular value decomposition has been employed on face images and a threshold singular value is determined empirically to reject or accept test images. Principal component analysis is used for feature extraction. Accepted test images are classified based on the majority vote criteria using different observation sequences of image features. Experimental findings on Yale and ORL databases in noisy such as Salt and Pepper and noise free environments reveal that the recognition accuracy of the proposed model is comparable to the existing techniques with reduced computational cost.

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