Royal Bank of Canada

Christopher Côté Srinivasa

Sayedmasoud Hashemi Amroabadi

Edison U Ortiz

Iustina-Miruna Vintila

Margaret Inez Salter

Arya Pourtabatabaie

Weiguang Ding

Sara Zafar Jafarzadeh

Giuseppe Marcello Antonio Castiglione

Ga Wu

An approach for increasing security of biometric templates is described. An improved system is adapted to split a full set of features or representations of a trained model into a first partial template and a second partial template, the second partial template being stored on a secure enclave accessible only through zero-knowledge proof based interfaces. During verification using the template, a new full set of features is received for comparison, and a model is loaded based on the available portions of the model. Comparison utilizing the second partial template requires the computation of zero-knowledge proofs as direct access to the underlying second partial template is prohibited by the secure enclave.

Facial recognition tokenization

A system and method for adversarial vulnerability testing of machine learning models is proposed that receives as an input, a representation of a non-differentiable machine learning model, transforms the input model into a smoothed model and conducts an adversarial search against the smoothed model to generate an output data value representative of a potential vulnerability to adversarial examples. Variant embodiments are also proposed, directed to noise injection, hyperparameter control, and exhaustive/sampling-based searches in an effort to balance computational efficiency and accuracy in practical implementation. Flagged vulnerabilities can be used to have models re-validated, re-trained, or removed from use due to an increased cybersecurity risk profile.

System and method for adversarial vulnerability testing of machine learning models

Growing community of inventors

Toronto, Canada

Christopher Côté Srinivasa