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The paper introduces a privacy-preserving pipeline for crowd monitoring that counts individuals across different locations and times without revealing their identities. The system extracts facial features, derives an identifier using a fuzzy extractor, and deletes the original facial image. These identifiers are then inserted into homomorphically encrypted Bloom filters, enabling oblivious set membership testing on encrypted data for counting purposes. The initial evaluation demonstrates the potential of this approach.
Count people across locations without revealing their identities using face recognition and homomorphic encryption.
An important aspect of crowd monitoring is knowing how many people we are dealing with. Sometimes, knowing the size of a crowd in a single location and at a specific moment is enough. Matters become problematic when counting the same people across dif ferent locations or counting them over longer periods of time. In those cases, we need to identify and later reidentify a person, which immediately leads to privacy concerns. Until recently, solutions have been based on unique identification of carry-on devices, yet privacy improvements have caused transmitted information to be randomized, rendering this technique mostly useless. We propose to use biometric data instead. We introduce a pipeline that counts people based on face recognition, yet without ever being able to reveal the identity of individuals. To count, a camera initially detects a face, extracts its features, and derives an identifier using a fuzzy extractor. The original facial image is then deleted. Identifiers are inserted into homomorphically encrypted Bloom filters. This allows oblivious set membership testing directly on encrypted data, enabling the system to count across locations or across different moments, without revealing any identities. We provide an initial evaluation of our method that shows promising results.