  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.
