In an age where technology and privacy intersect, our Secure Face ID Verification Demo is a testament to our unwavering commitment to privacy. Amid ubiquitous digital cameras, our mission is clear: empower individuals with facial recognition while safeguarding their sensitive data.
At the heart of our innovation lies a client-server architecture that redefines privacy and technology. The client, securely nested on the user's device, safeguards the client key for encryption. Simultaneously, the server, fortified and secure, handles face recognition within the encrypted domain.
We harness Fully Homomorphic Encryption (FHE), a monumental technological advance, to fortify privacy. FHE serves as an impenetrable shield, enabling computation on encrypted data without decryption. This ensures perpetual encryption during data calculation in the server, safeguarding personal information while achieving encrypted face matching.
Our Secure Face ID Verification Demo offers a versatile platform housing two pivotal functions, meticulously designed with privacy and security as their guiding principles:
Users can trust our database with their photos, knowing their facial features are shielded. Instead of sending raw images, our system converts them into encrypted vectors locally, preserving unique attributes. These vectors are securely encrypted using the client's key, remaining invulnerable as they travel to our fortified backend server for diligent safeguarding.
Our dedication to privacy is evident as users perform face matching with alternative images. The client extracts facial features locally, secures the embedding vector through encryption, and sends it securely to our server. Within the ciphertext domain, the server expertly executes the face matching algorithm while preserving data secrecy. After meticulous processing, the server delivers an encrypted result, exclusively decryptable with the client's key to confirm a match.
In our pursuit of excellence, we embrace cutting-edge innovation. Our face extraction relies on the versatile face-api.js with the Resnet34 model, while the robust cosine distance metric enhances our face matching algorithm. For Fully Homomorphic Encryption (FHE), we trust the advanced tfhe-rs library developed by Zama. These techniques, seamlessly woven with our unwavering privacy commitment, redefine facial recognition technology.
In a world where privacy and technology clash, our Secure Face ID Verification Demo showcases their synergy. Join us on a journey to a future where facial recognition realizes its potential while safeguarding personal privacy. Explore this groundbreaking solution and witness the future of secure, privacy-preserving face recognition firsthand.
If you would like to know more about the solution we are building or if you want to collaborate in any way, please feel free to reach out on email support@privasea.tech or fill in the form here
