Abstract
Leveraging on function-hiding Functional Encryption (FE) and inner-product-based matching, this work presents a practical privacy-preserving face identification system with two key novelties: switching functionalities of encryption and key generation algorithms of FE to optimize matching latency while maintaining its security guarantees, and identifying output leakage to later formalize two new attacks based on it with appropriate countermeasures. We validate our scheme in a realistic face matching scenario, attesting its applicability to pseudo real-time one-use face identification scenarios like passenger identification.
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Notes
- 1.
More info in https://en.wikipedia.org/wiki/Biometrics#Performance.
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Acknowledgements
The authors thank Vincent Despiegel for his valuable help towards giving birth to this work. Moreover, we express our gratitude to the willful guidance of Zekeriya Erkin. This work has also been partially supported by the 3IA Cöte d’Azur program (reference number ANR19-P3IA-0002).
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Ibarrondo, A., Chabanne, H., Önen, M. (2021). Practical Privacy-Preserving Face Identification Based on Function-Hiding Functional Encryption. In: Conti, M., Stevens, M., Krenn, S. (eds) Cryptology and Network Security. CANS 2021. Lecture Notes in Computer Science(), vol 13099. Springer, Cham. https://doi.org/10.1007/978-3-030-92548-2_4
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