privacy-preserving face recognition
Post on 18-Mar-2016
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Privacy-Preserving Face Recognition
Zekeriya Erkin1, Martin Franz2, Jorge Guajardo3,
Stefan Katzenbeisser2, Inald Lagendijk1, and Tomas Toft4
Introduction
Alice Bob
Owns a face imageIs neither willing to share the image nor the detection result
Owns a face databaseIs not willing to reveal his data
Run face recognition to determine
whether the face image is in database?
FIND
Paillier cryptosystem
• additively homomorphic public-key encryption schemes
• [a + b] = [a][b],• [ab] = [a]b. ( b is a constant)
Face Recognition
• Run Principal Component Analysis (PCA) from a set of criminal images to obtain eigenface
• Projects face images onto eigenfaces.
PrincipalComponent Analysis (PCA)
• Θ1,Θ2, . . . , ΘM : vectors of length N• average of the training images• covariance matrix• Run PCA• To determine the face space, we selectK << M eigenvectors u1, . . . , uK that correspond to the K largest eigenvalues.
criminal projection
• Θ1,Θ2, . . . , ΘM are projected onto the subspace spanned by the basis u1, . . . , uK to obtain their feature vector representation Ω1, . . . , ΩM.
Suspect Projection
• input image Γ
Progection in the encrypted domain
• input image
Calculating distances
Client’s
Calculating distances(conti)
BobAlice
Calculating distances
•DEMO~
Paillier cryptosystem
• Two large prime number p, q• n = p*q• Select random integer g • •
• Encryption• Decryption
Private key
Public key
Paillier cryptosystem
• Homomorphic addition of plaintexts
• Homomorphic multiplication of plaintexts
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