Differentially private models for detection of previously seen data

Jumio is constantly facing fraudulent attacks of repeated nature when a series of similar images with minor changes are submitted. To be able to respond effectively it is necessary to be able to learn previously seen fraudulent data. At the same time we have to deal with very sensitive private data and therefore it’s becoming a major concern for multiple reasons.

First, just comparing to all already seen data is not feasible technically due to optimization issues. And second most importantly it would cause legal issues due to privacy concerns and restrictions.

Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Jumio

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

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