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E Other Optimizations 29 2 5. Homomorphic Evaluation of the AES Circuit
1 Introduction In his breakthrough result [13], Gentry demonstrated that fully-homomorphic encryption was theoretically possible, assuming the hardness of some problems in integer lattices. Since then, many different improvements have been made, for example authors have proposed new variants, improved efficiency, suggested other hardness assumptions, etc. Some of these works were accompanied by implementation [26, 14, 8, 27, 19, 9], but all the implementations so far were either “proofs of concept” that can compute only one basic operation at a time (at great cost), or special-purpose implementations limited to evaluating very simple functions. In this work we report on the first implementation powerful enough to support an “interesting real world circuit”. Specifically, we implemented a variant of the leveled FHE-withoutbootstrapping scheme of Brakerski, Gentry, and Vaikuntanathan [5] (BGV), with support for deep enough circuits so that we can evaluate an entire AES-128 encryption operation. Why AES? We chose to shoot for an evaluation of AES since it seems like a natural benchmark: AES is widely deployed and used extensively in security-aware applications (so it is “practically relevant” to implement it), and the AES circuit is nontrivial on one hand, but on the other hand not astronomical. Moreover the AES circuit has a regular (and quite “algebraic”) structure , which is amenable to parallelism and optimizations. Indeed, for these same reasons AES is often used as a benchmark for implementations of protocols for secure multi-party computation (MPC), for example [24, 10, 17, 18]. Using the same yardstick to measure FHE and MPC protocols is quite natural, since these techniques target similar application domains and in some cases both techniques can be used to solve the same problem. Beyond being a natural benchmark, homomorphic evaluation of AES decryption also has interesting applications: When data is encrypted under AES and we want to compute on that data, then homomorphic AES decryption would transform this AES-encrypted data into an FHE-encrypted data, and then we could perform whatever computation we wanted. (Such applications were alluded to in [19, 27, 6]). Why BGV? Our implementation is based on the (ring-LWE-based) BGV cryptosystem [5], which at present is one of three variants that seem the most likely to yield “somewhat practical” homomorphic encryption. The other two are the NTRU-like cryptosystem of Lòpez-Alt et al. [21] and the ring-LWE-based fixed-modulus cryptosystem of Brakerski [4]. (These two variants were not yet available when we started our implementation effort.) These three different variants offer somewhat different implementation tradeoffs, but they all have similar performance characteristics. At present we do not know which of them will end up being faster in practice, but the differences are unlikely to be very significant. Moreover, we note that most of our optimizations for BGV are useful also for the other two variants. Our Contributions. Our implementation is based on a variant of the BGV scheme [5, 7, 6] (based on ring-LWE [22]), using the techniques of Smart and Vercauteren (SV) [27] and Gentry, Halevi and Smart (GHS) [15], and we introduce many new optimizations. Some of our optimizations are specific to AES, these are described in Section 4. Most of our optimization, however, are more general-purpose and can be used for homomorphic evaluation of other circuits, these are described in Section 3. Many of our general-purpose optimizations are aimed at reducing the number of FFTs and CRTs that we need to perform, by reducing the number of times that we need to convert polynomials between coefficient and evaluation representations. Since the cryptosystem is defined over a polynomial ring, many of the operations involve various manipulation of integer polynomials, such as modular multiplications and additions and Frobenius maps. Most of these operations can be performed more efficiently in evaluation 1 5. Homomorphic Evaluation of the AES Circuit
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AFRL-RI-RS-TR-2013-191 ADVANCED HOM
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REPORT DOCUMENTATION PAGE Form Appr
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12. An Equational Approach to Secur
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1.0 Executive Summary The ultimate
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The PROCEED program efforts are bro
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Towards the end of the project we i
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feature of the framework is that it
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4.3 Publications Figure 1: A block
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encrypted data. We introduce a prec
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present an attack showing that a pa
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alternatives: mis (based on mutual
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5.0 Conclusions and Recommendations
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[14] Bogdanov, Andrej, and Lee, Chi
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8.0 List of Symbols, Abbreviations,
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Public Affairs Approvals for papers
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Contents 1 Introduction 1 1.1 Our M
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t j = P (a j ), and finally interpo
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apparent problem by replacing the M
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As noted above, there is a multilin
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f(x 1 , . . . , x n ) ∈ F and eve
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Translation. Pick up from the publi
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Lemma 4. Let prime p = ω(N 2 ). Th
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[vDGHV10] Marten van Dijk, Craig Ge
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have u 2 − v 2 = 1 → (u − v)(
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inary), and we want to compute g c
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Fully Homomorphic Encryption withou
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scheme have bit length Ω(D) to all
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fast. Thus, the actual magnitude of
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Definition 3 (B-bounded distributio
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achieve 2 λ security against known
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Proof. 〈c 2 , s 2 〉 = BitDecomp
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• FHE.Add(pk, c 1 , c 2 ): Takes
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The computation needed to compute t
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If a and b are large enough, B domi
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5.1.2 How Batching Works Deploying
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We have the following lemma. Lemma
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Unpack(c, {τ σi→1 (s 1 )→s 2
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Since ‖e q1 ‖ < r q1 ,in, e q1
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[22] Damien Stehlé and Ron Steinfe
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1 Introduction Fully homomorphic en
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encryptions of the same message usi
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and Tromer [CT10] in a completely d
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is negligible in k, where Expt CPA
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2.3 Non-Interactive Simulation-Soun
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is defined as (state 1 , m 1 , . .
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scheme has almost-all-keys perfect
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The algorithm S 1 . The algorithm S
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The distribution D 6 . This distrib
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We resolve this issue using the app
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Page 101 and 102: • Homomorphic evaluation: The hom
Page 103 and 104: The number of repeated homomorphic
Page 105 and 106: [Gro04] [Gro10] J. Groth. Rerandomi
Page 107 and 108: Trapdoors for Lattices: Simpler, Ti
Page 109 and 110: mentioned, two central objects are
Page 111 and 112: Dimension m Quality s Length β ≈
Page 113 and 114: defined by G (or the semi-random ma
Page 115 and 116: 1.4 Other Related Work Concrete par
Page 117 and 118: Cryptographic problems. For β > 0,
Page 119 and 120: andom over G m s , for uniformly ra
Page 121 and 122: 3 Search to Decision Reduction Here
Page 123 and 124: • Preimage sampling for f G (x) =
Page 125 and 126: Note that for x ∈ {0, . . . , q
Page 127 and 128: The offline ‘bucketing’ approac
Page 129 and 130: G is primitive, the tag H in the ab
Page 131 and 132: conditional distribution of R given
Page 133 and 134: and parallelism of Algorithm 3 come
Page 135 and 136: is a coset of the lattice Λ = L( [
Page 137 and 138: scheme is su-scma-secure if Adv su-
Page 139 and 140: a discrete Gaussian with parameter
Page 141 and 142: • G ∈ Z n×nk q is a gadget mat
Page 143 and 144: must return this e (but possibly di
Page 145 and 146: [Gen09b] C. Gentry. Fully homomorph
Page 147 and 148: [vGHV10] M. van Dijk, C. Gentry, S.
Page 149: Contents 1 Introduction 1 2 Backgro
Page 153 and 154: Then in Section 3 we describe our
Page 155 and 156: In some more detail, the BGV key sw
Page 157 and 158: itself has low norm. In BGV [5] thi
Page 159 and 160: 3.4 Randomized Multiplication by Co
Page 161 and 162: One subtle implementation detail to
Page 163 and 164: ciphertexts. Producing the encrypte
Page 165 and 166: [24] Benny Pinkas, Thomas Schneider
Page 167 and 168: A.4 Sampling From A q At various po
Page 169 and 170: To maintain the noise estimate, the
Page 171 and 172: variance σ 2 φ(m), so 2d 2 (ζ)ɛ
Page 173 and 174: We stress that the only place where
Page 175 and 176: C.1.1 LWE with Sparse Key The analy
Page 177 and 178: The Smallest Modulus. After evaluat
Page 179 and 180: down by a factor of p t . Let’s r
Page 181 and 182: to apply a map κ i we express it a
Page 183 and 184: 1 Introduction Fully homomorphic en
Page 185 and 186: 4. No restrictions on the secret ke
Page 187 and 188: We point out that an alternative so
Page 189 and 190: Recall that GapSVP γ is the (promi
Page 191 and 192: Proof. By definition ⟨c, (1, s)
Page 193 and 194: • SI-HE.Enc pk (m): Identical to
Page 195 and 196: Proof of Theorem 4.2. Consider the
Page 197 and 198: We can now plug in what we know abo
Page 199 and 200: In particular (recall that E < ⌊q
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[Reg03] Oded Regev. New lattice bas
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1 Introduction An encryption scheme
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need to have errors. Since the expe
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Again we allow some “slackness”
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Learning Linear Functions. the clas
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Let us first outline the proof of T
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P ′ . 4 Namely H n ′ :n = P ′
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In conclusion, we get a sub-scheme
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[BV11b] [Gen09] [Gen10] [GH11] [GHS
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Quantum-Secure Message Authenticati
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the success probability away from 1
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Functions will be denoted by capito
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Quantum chosen message queries. In
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Since the w are the possible result
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number of distinct component values
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4.1 A Tight Upper Bound Theorem 4.1
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The algorithm is similar to the alg
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As we have already seen, for expone
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where S i (m) = (R i (m), PRF(k, (R
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To prove this theorem, we treat Y a
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Now we use this attack to obtain an
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Using this lemma we show that (3q +
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[KK11] Daniel M. Kane and Samuel A.
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1 Introduction Cloud storage system
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subset of the server’s storage, t
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security does not suffice. The main
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Static Data. PoR and PDP schemes fo
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For any efficient adversarial serve
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Overview of Construction. Let (Enc,
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means that one of the audit protoco
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Now we claim that an execution of E
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having higher capacity. The tables
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OWrite(i 1 , . . . , i t ; v 1 , .
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j-th level table again. With this o
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and distribute reprints for Governm
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A Simple Dynamic PoR with Square-Ro
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from Section 6 that is NRPH secure.
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The LWE problem was introduced in t
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1.2 Techniques and Comparison to Re
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(large) uniform errors as in [10],
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Proof. Let A be an algorithm runnin
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that 1 + ɛ = (1 + ɛ 1 )(1 + ɛ 2
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Because we are concerned with small
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For each i, since gcd(z i , q) = 1
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Proof. Let ω n = ω( √ log n) sa
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Theorem 3.8. Let m, n be integers,
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σ · O( √ m), except with probab
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k = n/2, and it suffices to set the
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[23] C. Peikert and B. Waters. Loss
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1 Introduction Consider the followi
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In the online phase clients individ
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the clients jointly run a secure co
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to compute F from scratch. The work
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I. Pre-processing phase. In this ph
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Online Phase: 1. Each client D i ge
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2n ciphertexts in order to be sure
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[KMR11] [KMR12] [Lip12] [LTV12] Sen
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For any set of adversarial parties
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C.5 Secure Computation We make use
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Experiment H 4 . This experiment is
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of A only in the case when A guesse
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leading to concrete implementations
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y modeling each block by an interac
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z i P 2 Q 1 y i P 1 s 1 r 1 x i w i
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2 Distributed Systems, Composition,
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[G | H](y) = (w, x): z = y w = y x[
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infinite chains, such as (M ∗ ;
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2.3 Multi-party computation, securi
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BCast(x) = (y 1 , . . . , y n ): y
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Sim(y A , s A ) = (y ′ A , r A):
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WCast(x ′ , w) = (y 1 ′ , . . .
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s ′ 0 s ′ A r ′ A y ′ H s
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p r ′ A Net’ s ′ r ′ H r A
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Notice that we have already underta
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simpler protocol description. For t
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[29] Andrew Chi-Chih Yao. Protocols
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2 Mihir Bellare, Stefano Tessaro, a
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10 Mihir Bellare, Stefano Tessaro,
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Multi-Instance Security and its App
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Multi-instance Security and Passwor
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a hash-of-hash construction: H 2 (M
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guisher queries (our lower bounds r
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in so doing they accidentally intro
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of D is defined as [ [ ] AdvH[P],R,
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main POW H[P],n,l1 : P 1 P 2 X 2
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now,thinkforconvenienceofw = l).The
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aroundO(q 2 )queries.Ideally, wewou
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HMAC l (K,Y 0) Y 0 F K Y 0 ′ G K
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This material is based on research
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24. Ari Juels and John G. Brainard.
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Contents 1 The BGV Homomorphic Encr
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‖a‖ canon 2 . The basic operati
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EncryptedArray/EncrytedArrayMod2r R
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g i ’s and h i ’s in one list,
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2.6 IndexSet and IndexMap: Sets and
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turn, are sometimes partitioned fur
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DoubleCRT& operator-=(const ZZ &num
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homomorphic automorphism operation
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For reasons that will be discussed
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where D i is the size of the i’th
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When key-switching a ciphertext ⃗
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constant (i.e. |poly(τ m )| 2 ), o
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ool isCorrect() const; and double l
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For the noise estimate in the new c
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The first time that ImportSecKey is
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EncryptedArray(const FHEcontext& co
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The MUX operation is implemented by
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5.1 Homomorphic Operations over GF
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EncryptedArrayMod2r ea(context); lo
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H/2m each and 0 with probability 1
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So now consider the inner sum in (7
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