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Fully Homomorphic Encryption Lecture 21 Recall Learning With - PowerPoint PPT Presentation

Fully Homomorphic Encryption Lecture 21 Recall Learning With Errors -s = A b A r A b e 1 LWE (decision version): (A,A s + e ) (A, r ), where A random m n , s uniform, e has small entries from a matrix in A Z q


  1. Fully Homomorphic Encryption Lecture 21

  2. Recall Learning With Errors -s ≈ = A b A r A b e 1 LWE (decision version): (A,A s + e ) ≈ (A, r ), where A random m × n , s uniform, e has “small” entries from a matrix in A ∈ Z q Gaussian distribution, and r uniform. Average-case solution for LWE ⇒ Worst-case solution for GapSVP (for appropriate choice of parameters)

  3. Recall Learning With Errors ≈ = M A r M z e m × n’ and z ∈ Z q n’ s.t. entries of A pseudorandom matrix M ∈ Z q M z are all small

  4. 
 
 
 Gentry-Sahai-Waters Want to allow homomorphic operations on the ciphertext Rough plan: Ciphertext is a matrix. Addition and multiplication of messages by addition and multiplication of ciphertexts m × n and random z ∈ Z q n s.t. Recall from LWE: pseudorandom M ∈ Z q z T M T has small entries 
 e T z T = M T m × n and private key z Public key M ∈ Z q Enc( μ ) = M T R + μ G where R ← {0,1} m × km and G ∈ Z q n × km the matrix n × d → Z q km × d to reverse bit-decomposition operation B : Z q Dec z (C) : z T C = δ T + μ z T G where δ T =e T R

  5. Gentry-Sahai-Waters Supports messages μ ∈ {0,1} and NAND operations up to an a priori bounded depth of NANDs m × n and private key z s.t. z T M has small entries Public key M ∈ Z q Enc( μ ) = M T R + μ G where R ← {0,1} m × km (and G ∈ Z q n × km the matrix to reverse bit-decomposition) Dec z (C) : z T C = δ T + μ z T G where δ T =e T R NAND(C 1 ,C 2 ) : G - C 1 ⋅ B(C 2 ) (G is a (non-random) encryption of 1) z T C 1 ⋅ B(C 2 ) = z T C 1 ⋅ B(C 2 ) = ( δ 1T + μ 1 z T G) B(C 2 ) 
 Only “left depth” = δ 1T B(C 2 ) + μ 1 z T C 2 = δ T + μ 1 μ 2 z T G 
 counts, since 
 δ ≤ k ⋅ m ⋅ δ 1 + δ 2 where δ T = δ 1T B(C 2 ) + μ 1 δ 2T has small entries In general, error gets multiplied by km. Allows depth ≈ log km q

  6. Bootstrapping Removing the need for an a priori bound Main idea: Can “refresh” the ciphertext to reduce noise Refresh: homomorphically decrypt the given ciphertext under a fresh layer of encryption cf. Degree reduction via share-switching: Homomorphically reconstruct under a fresh layer of sharing But here, we have a secret-key (and there is only one party who knows the ciphertext fully) Ciphertext is known, but secret-key should be kept encrypted Consider decryption of a given ciphertext as a function applied to the secret-key: D C (sk) := Dec(C,sk)

  7. 
 
 
 
 
 
 Bootstrapping Given a ciphertext C and hence the decryption function D C s.t. 
 D C (sk) := Dec(C,sk) μ Also given: an encryption of sk (beware: circularity!) Goal: a fresh ciphertext with message D C (sk) 
 D C Enc( μ ) Refreshed: Doesn’ t depend sk on how unfresh C was, but only on the depth of D C Homomorphic D C evaluation in the ciphertext space Fresh encryption of Enc(sk) sk, provided along with the public key

  8. 
 
 
 
 
 Bootstrapping If depth of D C s.t. D C (sk) := Dec(C,sk) is strictly less than the depth allowed by the homomorphic encryption scheme, a ciphertext C μ can be strictly refreshed Then can carry out at least one more operation on 
 D C such ciphertexts (before refreshing again) 
 Enc( μ ) Refreshed: Doesn’ t depend sk on how unfresh C was, but only on the depth of D C Homomorphic D C evaluation in the ciphertext space Fresh encryption of Enc(sk) sk, provided along with the public key

  9. 
 
 
 
 
 
 Bootstrapping Circularity: Encrypting the secret-key of a scheme under the scheme itself Can break security in general! μ LWE does not by itself imply security D C Stronger assumption: “Circular Security of LWE” 
 Enc( μ ) Refreshed: Doesn’ t depend sk on how unfresh C was, but only on the depth of D C Homomorphic D C evaluation in the ciphertext space Fresh encryption of Enc(sk) sk, provided along with the public key

  10. Bootstrapping GSW Supports log(k) depth computation with poly(k) complexity Need low depth decryption (as a function of secret-key) Dec z (C) : z T C = δ T + μ z T G where δ T =e T R And then check if the result is close to 0 T or z T G How? Multiply by B( w ) where last coordinate of w is ⌊ q/2 ⌋ and other coordinates 0 z T C B( w ) = δ T B( w ) + μ z T w = ε + μ ⌊ q/2 ⌋ Has most significant bit = μ (since error | ε | << q/ 4) Dec z (C) : MSB( z T C B( w ) ). All operations mod q. If q were small (poly(k)) this would be small depth (log(k)) Problem: q is super-polynomial in security parameter k Idea: Can change modulus for decryption!

  11. Modulus Switching for GSW Dec z (C) : MSB( z T Y mod q), where Y = C B( w ) z T Y = ε 0 + μ (q/2) + aq (for some a ∈ Z ) To switch to a smaller modulus p < q: Consider Y’ = ⌈ (p/ q) Y ⌋ . Let Δ = Y’-(p/ q)Y . q) z T Y + z T Δ 
 z T Y’ = (p/ = ε 1 + μ (p/2) + ap where ε 1 = (p/ q) ε 0 + z T Δ n . Need z T Δ to be small. But z T = [ - s T 1 ] for s uniform in Z q Fix: LWE with small s is as good as with uniform s [Exercise] Final bootstrapping: Given C, let Y’ = ⌈ (p/ q) C B( w ) ⌋ where p small (poly(k)). Define function D Y’ which does decryption mod p. Homomorphically evaluate D Y’ on encryption of z mod p (encryption is mod q).

  12. FHE in Practice Several implementations in recent years Prominent ones based on schemes of Fan-Vercauteren (FV) and Brakerski-Gentry-Vaikuntanathan (BGV) with various subsequent optimisations BGV implementations: HELib (IBM), Λ o λ FV implementations: SEAL (Microsoft), FV-NFLlib (CryptoExperts), HomomorphicEncryption R Package … Both based on “Ring LWE” Moderately fast E.g., HELib can apply AES (encipher/decipher) to about 200 plaintext blocks using an encrypted key in about 20 minutes (a bit faster without bootstrapping, if no need to further compute on the ciphertext)

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