Privacy Preserving Face Recognition Utilizing Differential Privacy

Abstract

Facial recognition technologies are implemented in many areas, including but not limited to, citizen surveillance, crime control, activity monitoring, and facial expression evaluation. However, processing biometric information is a resource-intensive task that often involves third-party servers, which can be accessed by adversaries with malicious intent. Biometric information delivered to untrusted third-party servers in an uncontrolled manner can be considered a significant privacy leak (i.e. uncontrolled information release) as biometrics can be correlated with sensitive data such as healthcare or financial records. In this paper, we propose a privacy-preserving technique for "controlled information release", where we disguise an original face image and prevent leakage of the biometric features while identifying a person. We introduce a new privacy-preserving face recognition protocol named PEEP (Privacy using EigEnface Perturbation) that utilizes local differential privacy. PEEP applies perturbation to Eigenfaces utilizing differential privacy and stores only the perturbed data in the third-party servers to run a standard Eigenface recognition algorithm. As a result, the trained model will not be vulnerable to privacy attacks such as membership inference and model memorization attacks. Our experiments show that PEEP exhibits a classification accuracy of around 70% - 90% under standard privacy settings.

References in corpus (8)

• Deep Learning with Differential Privacy
• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
• Extremal Mechanisms for Local Differential Privacy
• Privacy Preserving Distributed Machine Learning with Federated Learning
• Efficient privacy preservation of big data for accurate data mining
• Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining
• An Efficient and Scalable Privacy Preserving Algorithm for Big Data and Data Streams
• MVG Mechanism: Differential Privacy under Matrix-Valued Query

Cited by in corpus (8)

• Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges
• Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
• Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention
• DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain
• GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions
• FaceTracer: Unveiling Source Identities from Swapped Face Images and Videos for Fraud Prevention
• Differentially Private ADMM Algorithms for Machine Learning
• PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval