HERS: Homomorphically Encrypted Representation Search
@article{Engelsma2020HERSHE, title={HERS: Homomorphically Encrypted Representation Search}, author={Joshua J. Engelsma and Anil K. Jain and Vishnu Naresh Boddeti}, journal={IEEE Transactions on Biometrics, Behavior, and Identity Science}, year={2020}, volume={4}, pages={349-360} }
We present a method to search for a probe (or query) image representation against a large gallery in the encrypted domain. We require that the probe and gallery images be represented in terms of a fixed-length representation, which is typical for representations obtained from learned networks. Our encryption scheme is agnostic to how the fixed-length representation is obtained and can therefore be applied to any fixed-length representation in any application domain. Our method, dubbed HERS…
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References
SHOWING 1-10 OF 87 REFERENCES
Secure Face Matching Using Fully Homomorphic Encryption
- Computer ScienceBTAS
- 2018
The practicality of using a fully homomorphic encryption based framework to secure a database of face templates, designed to preserve the privacy of users and prevent information leakage from the templates, while maintaining their utility through template matching directly in the encrypted domain is explored.
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale
- Computer Science2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2016
The MegaFace dataset is assembled, both for identification and verification performance, and performance with respect to pose and a persons age is evaluated, as a function of training data size (#photos and #people).
Learning a Fixed-Length Fingerprint Representation
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2021
DeepPrint incorporates fingerprint domain knowledge, including alignment and minutiae detection, into the deep network architecture to maximize the discriminative power of its representation, which is the most compact and discrim inative fixed-length fingerprint representation reported in the academic literature.
On the Intrinsic Dimensionality of Image Representations
- Computer Science2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2019
A deep neural network based non-linear mapping is developed, dubbed DeepMDS, that transforms the ambient representation to the minimal intrinsic space, and the veracity of the mapping through image matching in the intrinsic space is validated.
Homomorphic Encryption for Arithmetic of Approximate Numbers
- Computer Science, MathematicsASIACRYPT
- 2017
We suggest a method to construct a homomorphic encryption scheme for approximate arithmetic. It supports an approximate addition and multiplication of encrypted messages, together with a new…
Image Reconstruction from Bag-of-Visual-Words
- Computer Science2014 IEEE Conference on Computer Vision and Pattern Recognition
- 2014
This work uses a large-scale image database to estimate the spatial arrangement of local descriptors and proposes a heuristic but efficient method to optimize it, which can reconstruct the original images, although the image features lack spatial information and include quantization errors.
IronMask: Modular Architecture for Protecting Deep Face Template
- Computer Science2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2021
This paper presents a modular architecture for face template protection, called IronMask, that can be combined with any face recognition system using angular distance metric, and circumvent the need for binarization by proposing a new real-valued error-correcting-code that is compatible withreal-valued templates and can therefore, minimize performance degradation.
Efficient Encrypted Inference on Ensembles of Decision Trees
- Computer ScienceArXiv
- 2021
This work proposes a framework to transfer knowledge extracted by complex decision tree ensembles to shallow neural networks (referred to as DTNets) that are highly conducive to encrypted inference and minimizes the accuracy loss by searching for the best DTNet architecture that operates within the given depth constraints.
Efficient CNN Building Blocks for Encrypted Data
- Computer ScienceArXiv
- 2021
This work considers a Machine Learning as a Service (MLaaS) scenario where both input data and model parameters are secured using FHE, and shows that operational parameters of the chosen FHE scheme such as the degree of the cyclotomic polynomial, depth limitations of the underlying leveled HE scheme, and the computational precision parameters have a major impact on the design of the machine learning model.
Efficiency Analysis of Post-quantum-secure Face Template Protection Schemes based on Homomorphic Encryption
- Computer Science, Mathematics2020 International Conference of the Biometrics Special Interest Group (BIOSIG)
- 2020
It is shown that a face verification in the encrypted domain requires only 50 ms transaction time and a template size of 5.5 KB, and homomorphic encryption allows to compute the distance between two protected templates in theencrypted domain without a degradation of biometric performance with respect to the corresponding system.