Efficient Methods for Privacy Preserving Face Detection

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

Bibtex Metadata Paper


Shai Avidan, Moshe Butman


Bob offers a face-detection web service where clients can submit their images for analysis. Alice would very much like to use the service, but is reluctant to reveal the content of her images to Bob. Bob, for his part, is reluctant to release his face detector, as he spent a lot of time, energy and money constructing it. Secure Multi- Party computations use cryptographic tools to solve this problem without leaking any information. Unfortunately, these methods are slow to compute and we intro- duce a couple of machine learning techniques that allow the parties to solve the problem while leaking a controlled amount of information. The first method is an information-bottleneck variant of AdaBoost that lets Bob find a subset of features that are enough for classifying an image patch, but not enough to actually recon- struct it. The second machine learning technique is active learning that allows Alice to construct an online classifier, based on a small number of calls to Bob’s face detector. She can then use her online classifier as a fast rejector before using a cryptographically secure classifier on the remaining image patches.