Accurate and Privacy-Preserving Person Localization Using Federated-Learning and the Camera Surveillance Systems of Public Places

Abstract

In this paper we propose an accurate and privacy-preserving scheme that enables a law enforcement agency to locate persons of interest using the camera surveillance systems of public places. Comparing to the existing schemes that measure the Euclidean distance to locate persons using their embedding vectors storing facial features, we use a more accurate approach by training a machine learning model. Moreover, to avoid leaking sensitive information by sharing the images of the public places’ visitors to train the model, we use a federated learning technique to compute the model in a privacy-preserving way. The model is designed in such a way that makes executing it over encrypted data efficient. Specifically, the model is executed by three parties as follows. Each public place computes an embedding vector for each visitor’s image and inputs it to a neural network and encrypts the output using a modified inner product encryption scheme and sends the ciphertext to a cloud server. The law enforcement agency does the same steps on the images of persons of interest. Finally, the server uses these ciphertexts to evaluate the last layer of the model by computing the inner product of the two vectors over encrypted data. The cryptosystem enables the server to compute the inner product of two vectors using their ciphertexts without being able to learn the vectors. We have modified an encryption cryptosystem that is designed for a single public place and a single law enforcement agency to make it more efficient in our application that has multiple public places. To evaluate our scheme, we have conducted extensive experiments and the results confirm that our model is accurate in locating persons of interest with low communication and computation overhead. A formal proof and analysis are used to demonstrate the ability of our scheme to preserve privacy.