Abstract

Given the availability of cameras in mobile phones, drones and Internet-connected devices, facial privacy has become an area of major interest in the last few years, especially when photos are captured and can be used to identify bystanders’ faces who may have not given consent for these photos to be taken and be identified. Some solutions to protect facial privacy in photos currently exist. However, many of these solutions do not give a choice to bystanders because they rely on algorithms that de-identify photos or protocols to deactivate devices and systems not controlled by bystanders, thereby being dependent on the bystanders’ trust in these systems to protect his/her facial privacy. To address these limitations, we propose FacePET (Facial Privacy Enhancing Technology), a wearable system worn by bystanders and designed to enhance facial privacy. We present the design, implementation, and evaluation of the FacePET and discuss some open research issues.

Highlights

  • According to Ericcson’s Mobility Report [1], there are more than four billion smartphones subscriptions in the world

  • To the best of our knowledge, knowledge, this is the first work that describes an Internet of Things (IoT) device to enhance the privacy of its wearer

  • We evaluated the effectiveness of the FacePET wearable prototype in protecting facial features by taking photos using digital cameras using different devices

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Summary

Introduction

According to Ericcson’s Mobility Report [1], there are more than four billion smartphones subscriptions in the world. The development of artificial intelligence techniques such as deep learning can expose individuals to privacy issues Among these issues is bystanders’ privacy [2,3] which is the issue that arises when a device collects sensor data (such as photos, sound or video) that can be used to identify bystanders who may have not given consent for them to be identified. In recent years, taking photographs in public that may include bystanders has once again been receiving attention, especially when it comes to privacy concerns of the bystanders This problem has become important because of the ubiquity of camera-enabled mobile and wearable devices, and the proliferation of social networks that allow photos to be instantly shared with the world instead of being kept private in a physical album (as was the case only a few decades ago) [2].

Methods currently available to handle bystanders’
Location Dependent Methods
Obfuscation Dependent Methods
Evaluation of Methods for Facial Privacy Protection
Face Detection and Recognition
Limitations
Haar-like features
Proposed
FacePET System’s Hardware Architecture
FacePET System’s Consent Protocol
Sequence
Evaluation of the FacePET System
FacePET
Limitations and Future
Conclusions
Full Text
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