Report

CRYPTOGRAPHIC MULTILINEAR MAPS: APPLICATIONS, CONSTRUCTION, CRYPTANALYSIS Craig Gentry, IBM Joint with Sanjam Garg (UCLA) and Shai Halevi (IBM) Diamant Symposium, Doorn Netherlands Cryptographic Bilinear Maps (Weil and Tate Pairings) Bilinear Maps in Cryptography Cryptographic bilinear map Groups G1, G2, GT of order l with canonical generators g1, g2, gT and a bilinear map e : G1 × G2 → GT where e(g1a,g2b) = gTab for all a,b 2 Z/lZ. At least, “discrete log” problems in G1,G2 are “hard”. Given g1, g1a for random a 2 [l], output a. Symmetric bilinear map: G1 = G2. (Call these “G”.) Instantiation: Weil or Tate pairings over elliptic curves. Bilinear Maps: “Hard” Problems Bilinear Diffie-Hellman: Given g, ga, gb, gc 2 G and g’2GT, distinguish whether g’ = e(g,g)abc. A “tripartite” extension of classical Diffie-Hellman problem: Given g, ga, gb, g’ 2 G, distinguish whether g’ = gab. Easy Application: Tripartite key agreement [Joux00]: Bob, Carol generate a,b,c and broadcast ga, gb, gc. They each separately compute the key K = e(g,g)abc. Alice, Other Apps of Bilinear Maps: IBE Identity-Based Encryption [Boneh-Franklin ‘01] Setup(1λ): H : {0,1}* → G be a hash function that maps ID’s to G. Authority generates secret a. MSK = a and MPK = ga. Let KeyGen(MSK,ID): Set gID = H(ID) 2 G. SKID = gIDa. Generate random c. Set K=e(ga,gID)c. Send CT = (gc, SymEncK(m)). Encrypt(MPK,ID,m): Decrypt(SKID,CT): Compute K = e(SKID,gc). Other Apps of Bilinear Maps: Predicate Encryption Predicate Encryption: a generalization of IBE. Setup(1λ, predicate function F): Authority generates MSK,MPK. x2{0,1}s): Authority uses MSK to generate key SKx for string x. (x could represent user’s “attributes”) KeyGen(MSK, Encrypt(MPK,y2{0,1}t, m): Encrypter generates ciphertext Cy for string y. (y could represent an “access policy”) Decrypt(SKx,Cy): Decrypt works (recovers m) iff F(x,y)=1. Predicate Encryption schemes using bilinear maps are “weak”. They can only enforce simple predicates computable by low-depth circuits. Cryptographic Multilinear Maps Definition/Functionality and Applications Multilinear Maps: Definition/Functionality Cryptographic n-multilinear map (for groups) Groups G1, …, Gn of order l with generators g1, …, gn Family of maps: ei,k : Gi × Gk → Gi+k for i+k ≤ n, where ei,k(gia,gkb) = gi+kab for all a,b 2 Z/lZ. At least, the “discrete log” problems in {Gi} are “hard”. Notation Simplification: e(gj1, …, gjt) = gj1+...+jt. Multilinear Maps over Sets Cryptographic n-multilinear map (for sets) Finite ring R and sets Ei for all i 2 [n]: “level-i encodings” Each set Ei is partitioned into Ei(a) for a 2 R: “level-i encodings of a”. Sampling: It should be efficient to sample a “level-0” encoding such that the distribution over R is uniform. Note: In the “group” Equality testing: It should be efficient to distinguish whether setting, there is onlytwo encodings encode the same thing at the same level.encoding one level-i Note: the “group” of a –Innamely, gia. setting, a level-0 Note: In the “group” encoding is just a setting, equality testing is number in [l]. trivial, since the encodings are literally the same. Multilinear Maps over Sets (cont’d) Cryptographic n-multilinear map (for sets) Addition/Subtraction: There are ops + and – such that: every i 2 [n], every a1, a2 2 R, every u1 2 Ei(a1), u2 2 Ei(a2): We have u1+u2 2 Ei(a1+a2) and u1-u2 2 Ei(a -a ). For 1 2 There is an op × such that: Analogous to (a1), u 2 E (a2): For every i+k ≤ n, every a1, a2 2 R, every u1 2 Eimultiplication 2 k and division We have u1×u2 2 Ei+k(a ∙a ). within a group. At least, the “discrete log” problems in {Sj} are “hard”. Analogous to Given level-j encoding of a, hard to compute level-0 encoding of a. the multilinear map function for groups Multiplication: 1 2 Multilinear Maps: Hard Problems n-Multilinear DH (for sets): Given level-1 encodings of 1, a1, …, an+1, and level-n encoding u, distinguish whether u encodes a1∙∙∙an+1. n-Multilinear DH (for groups): Given g1, g1a1,…, g1an+1 2 G1, and g’2Gn, distinguish whether g’ = gna1…an+1. Easy Application: (n+1)-partite key agreement [BonehSilverberg ‘03]: Party i generates level-0 encoding of ai, and broadcasts level-1 encoding of ai. Each party separately computes K = e(g1, …, g1) a1…an+1. Big Application: Predicate Encryption for Circuits Let F(x,y) be an arbitrarily complex boolean predicate function, computable in time Tf. There is a boolean circuit C(x,y) of size O(Tf log Tf) that computes F. Circuits have (say) AND, OR, and NOT gates Using a O(|C|)-linear map, we can construct a predicate encryption scheme for F whose performance is O(|C|) group operations. [Garg-Gentry-Halevi-2012, Sahai-Waters-2012] Multilinear Maps: Do They Exist? Boneh and Silverberg say it’s unlikely cryptographic m-maps can be constructed from abelian varieties: “We also give evidence that such maps might have to either come from outside the realm of algebraic geometry, or occur as ‘unnatural’ computable maps arising from geometry.” Whirlwind Tour of Lattice Crypto Focusing on NTRU and Homomorphic Encryption Lattices, and “Hard” Problems 0 A lattice is just an additive subgroup of Rn. Lattices, and “Hard” Problems v2’ v2 0 v1’ v1 In other words, any rank-n lattice L consists of all integer linear combinations of a rank-n set of basis vectors. Lattices, and “Hard” Problems v2’ v2 0 v1’ v1 Given some basis of L, it may be hard to find a good basis of L, to solve the (approximate) shortest/closest vector problems. Lattice Reduction [Lenstra,Lenstra,Lovász ‘82]: Given a rank-n lattice L, the LLL algorithm runs in time poly(n) and outputs a 2n-approximation of the shortest vector in L. [Schnorr’93]: Roughly, it 2k-approximates SVP in 2n/k time. NTRU [HPS98] Parameters: Integers N, p, q with p « q, gcd(p,q)=1. (Example: N=257, q=127, p=3.) Polynomial rings R = Z[x]/(xN-1), Rp = R/pR, and Rq = R/qR. Secret key sk: Polynomials f, g 2 R, where: f and g are “small”. Their coefficients are « q. f = 1 mod p and g = 0 mod p. Public key pk: Set h ← g/f 2 Rq. Encrypt(pk, m2Rp with coefficients in (-p/2,p/2)): Sample random “small” r from R. Ciphertext c ← m + rh. Decrypt(sk, c): Set e ← fc = fm+rg. Output m ← (e mod p). NTRU: Where are the Lattices? h = g/f 2 Rq → f(x)∙h(x) - q∙c(x) = g(x) mod (xN-1) 1 0 0 h0 h1 hN-1 f1 0 1 0 hN-1 h0 hN-2 fN-1 0 0 1 h1 h2 h0 c0 0 0 0 q 0 0 c1 0 0 0 0 q 0 0 0 0 0 0 q f0 f1 g0 g1 … f0 … cN-1 … fN-1 … gN-1 NTRU Security NTRU can be broken via lattice reduction (eventually) NTRU is semantically secure if ratios g/f 2 Rq of “small” elements are hard to distinguish from random elements of Rq. NTRU Parameters: Integers N, p, q with p « q, gcd(p,q)=1. (Example: N=257, q=127, p=3.) Polynomial rings R = Z[x]/(xN-1), Rp = R/pR, and Rq = R/qR. Secret key sk: Polynomials f, g 2 R, where: f and g are “small”. Their coefficients are « q. f = 1 mod p and g = 0 mod p. Public key pk: Set h ← g/f 2 Rq. Encrypt(pk, m2Rp with coefficients in (-p/2,p/2)): Sample random “small” r from R. Ciphertext c ← m + rh. Decrypt(sk, c): Set e ← fc = fm+rg. Output m ← (e mod p). NTRU Parameters: Integers N, p, q with p « q, gcd(p,q)=1. (Example: N=512, q=127, p=3.) Polynomial rings R = Z[x]/(ΦN(x)), Rp = R/pR, and Rq = R/qR. Secret key sk: Polynomials f, g 2 R, where: f and g are “small”. Their coefficients are « q. f = 1 mod p and g = 0 mod p. Public key pk: Set h ← g/f 2 Rq. Encrypt(pk, m2Rp with coefficients in (-p/2,p/2)): Sample random “small” r from R. Ciphertext c ← m + rh. Decrypt(sk, c): Set e ← fc = fm+rg. Output m ← (e mod p). NTRU Parameters: Integers N, q. “Small” p 2 R, with ideal I = (p) relative prime to (q). (Example: N=512, q=127) Polynomial rings R = Z[x]/(ΦN(x)), Rp = R/I, and Rq = R/qR. Secret key sk: Polynomials f, g 2 R, where: f and g are “small”. Their coefficients are « q. f 2 1+I and g 2 I. (g is a small multiple of p.) Public key pk: Set h ← g/f 2 Rq. Encrypt(pk, m2Rp with small coefficients): Sample random “small” r from R. Ciphertext c ← m + rh. Decrypt(sk, c): Set e ← fc = fm+rg. Output m ← (e mod I). NTRU Parameters: Integers N, q. “Small” p 2 R, with ideal I = (p) relative prime to (q). (Example: N=512, q=127) Polynomial rings R = Z[x]/(ΦN(x)), Rp = R/I, and Rq = R/qR. Secret key sk: Polynomials f, g 2 R, where: f and g are “small”. Their coefficients are « q. f 2 1+I and g 2 I. (g is a small multiple of p.) Public key pk: Set h0 ← g/f 2 Rq and h1 ← f/f 2 Rq. Encrypt(pk, m2Rp with small coefficients): Sample random “small” r from R. Ciphertext c ← mh1 + rh0. Decrypt(sk, c): Set e ← fc = fm+rg. Output m ← (e mod I). NTRU Parameters: Integers N, q. “Small” p 2 R, with ideal I = (p) relative prime to (q). (Example: N=512, q=127) Polynomial rings R = Z[x]/(ΦN(x)), Rp = R/I, and Rq = R/qR. Secret key sk: Random z 2 Rq. Polynomials f, g 2 R, where: f and g are “small”. Their coefficients are « q. f 2 1+I and g 2 I. (g is a small multiple of p.) Public key pk: Set h0 ← g/z 2 Rq and h1 ← f/z 2 Rq. Encrypt(pk, m2Rp with small coefficients): Sample random “small” r from R. Ciphertext c ← mh1 + rh0. Decrypt(sk, c): Set e ← zc = fm+rg. Output m ← (e mod I). NTRU NTRU Summary A ciphertext that encrypts m 2 Rp has the form e/z 2 Rq, where e is “small” (coefficients « q) and e 2 m+I. To decrypt, multiply z to get e. Then reduce e mod I. The public key contains encryptions of 0 and 1 (h0 and h1). To encrypt m, multiply m with h1 and add “random” encryption of 0. NTRU: Additive Homomorphism Given: Ciphertexts c1, c2 that encrypt m1, m2 2 Rp. ci = ei/z 2 Rq where ei is small and ei = mi mod p. Claim: Set c = c1+c2 2 Rq and m = m1+m2 2 Rp. Then c encrypts m. c = (e1+e2)/z where e1+e2=m mod p and e1+e2 is “sort of small”. It works if |ei| « q. NTRU: Multiplicative Homomorphism Given: Ciphertexts c1, c2 that encrypt m1, m2 2 Rp. ci = ei/z 2 Rq where ei is small and ei = mi mod p. Claim: Set c = c1∙c2 2 Rq and m = m1∙m2 2 Rp. Then c encrypts m under z2 (rather than under z). = (e1∙e2)/z2 where e1∙e2=m mod p and e1∙e2 is “sort of small”. It works if |ei| « √q. c NTRU: Any Homogeneous Polynomial Given: Ciphertexts c1, …, ct encrypting m1,…, mt. ci = ei/z 2 Rq where ei is small and ei = mi mod p. Claim: Let f be a degree-d homogeneous poly. Set c = f(c1, …, ct) 2 Rq and m = f(m1, …, mt) 2 Rp. Then c encrypts m under zd. = f(e1, …, et)/zd where f(e1, …, et)=m mod p and f(e1, …, et) is “sort of small”. It works if |ei| « q1/d. c Homomorphic Encryption The special sauce! For security parameter k, Eval’s running should be Time(f)∙poly(λ) “I want 1) the cloud to process my data 2) even though it is encrypted. Run Eval[ f, Enck(x) ] = Enck[f(x)] Enck(x) function f This could be encrypted too. Alice (Input: data x, key k) f(x) Enck[f(x)] Server (Cloud) Delegation: Should cost less for Alice to encrypt x and decrypt f(x) than to compute f(x) herself. Homomorphic Encryption from NTRU Homorphic NTRU Summary A level-d encryption of m 2 Rp has the form e/zd 2 Rq, where e is “small” (coefficients « q) and e 2 m+I. Given level-1 encryptions c1, …, ct of m1, …, mt, we can “homomorphically” compute a level-d encryption of f(m1, …, mt) for any degree-d polynomial f, if the initial ei’s are small enough. The “noise” – i.e., size of the numerator – grows exp. with degree. Noise control techniques: bootstrapping [Gen09], modulus reduction [BV12,BGV12]. Big open problem: Fast reusable way to contain the noise. “Noisy” Multilinear Maps (Similar to NTRU-Based HE, but with Equality Testing) Adding an Equality Test Given level-d encodings c1 = e1/zd and c2 = e2/zd, how do we test whether they encode the same m? Fact: If they encode same thing, then e1-e2 2 I. Moreover, (e1-e2)/p is a “small” polynomial. Zero-Testing parameter: aZT = b∙zd/p for “somewhat small b” Multiply the zero-testing parameter with (c1-c2). aZT(c1-c2) = b(e1-e2)/p has coefficients < q. If c1 and c2 encode different things, the denominator p ensures that the result does not have small coefficients. Example Application: (n+1)-partite DH Parameters: Rings R = Z[x]/(ΦN(x)), Rp = R/I, and Rq = R/qR, where p is “small” and I = (p) relative prime to (q). We don’t give out p. Level-1 encodings h0, h1 of 0 and 1. hi = ei/z, where ei = i mod I and is “small”. Party i samples a random level-0 encoding ai. Samples “small” ai 2 R via Gaussian distribution The coset of ai in Rp will be statistically uniform. Party i sends level-1 encoding of ai: aih1+rih0 2 Rq. Each party computes level-n encoding of a1∙∙∙an+1. Note: Noisiness of encoding is exponential in n. Example Application: (n+1)-partite DH Each party i has a level-n ei/zn encoding of a1∙∙∙an+1. Party i sets Ki’ = azt (ei/zn), and key Ki = MSBs(Ki’). Claim: Each party computes the same key. – Kj’ = azt (ei-ej)/zn = b(ei-ej)/p But ei, ej are “small” and both are in a1∙∙∙an+1+I. Ki ’ So, So, (ei-ej)/p is some “small” polynomial Eij. Ki’–Kj’ = b∙Eij, small. Ki’-Kj’ have the same most significant bits, with high probability. Big Application: Predicate Encryption for Arbitrarily Complex Functions Our “noisy” n-multilinear map permits predicate encryption for circuits of size up to n-1. Noisiness that is ok. of encodings grows exponentially with n, but Cryptanalysis: “Trivial” Attacks For example, can an eavesdropper “trivially” generate a level-n encoding of a (n+1)-partite Diffie-Hellman key? Trivial “Attacks” Eavesdropper in (n+1)-partite DH gets: Parameters: Level-1 encodings h0, h1 of 0 and 1. hi = ei/z, where ei = i mod I and is “small”. Zero-testing parameter: azt = bzn/p. Party i’s constribution: level-1 encoding ci/z of ai. Weighting of variables Set w(ei) = w(z) = w(p) = w(ci) = 1 and w(b) = 1-n. w(ei/z) = 0. Weight of all terms above is 0. Trivial “Attacks” Straight-line program (SLP) Only allowed to (iteratively) add, subtract, multiply, or divide pairs of elements that it has already computed. A SLP that is given weight 0 terms can only compute more weight 0 terms. The DH key is of the form K = e/zn, where e 2 a1∙∙∙an+1+I. The key cannot be expressed as a weight 0 term. Cryptanalysis: Nontrivial Attacks Algebraic and Lattice Attacks Attack Landscape All attacks on NTRU apply to our n-linear maps. Additional attacks: The principal ideal I = (p) is not hidden. azt = bzn/p, h0 = e0/z and h1 = e1/z with e0 = c0p. The terms azt∙h0i∙ h1n-i = b∙c0i∙pi-1∙e1n-I likely generate the ideal I. Recall An attacker that finds a good basis of I can break our scheme. There are better attacks on principal ideal lattices than on general ideal lattices. (But still inefficient.) Using a Good Basis of I Player i’s DH contribution: a level-1 encoding of ai. Easy to compute ai’s coset of I. (Notice: this is different from finding a “small” representative of ai’s coset, a level0 encoding of ai.) Compute level-(n-1) encodings of 1 and ai: e/zn-1, e’/zn-1. Multiply each of them with azt and h0 = c0p/z. We get bec0 and be’c0. Compute be’c0/bec0 = e’/e in Rp to get ai’s coset. Spoofing Player i: If we have a good basis of I, player i’s coset gives a level-0 encoding of ai. The attacker can spoof player i. Dimension-Halving for Principal Ideal Lattices [GS’02]: Given a basis of I = (u) for u(x) 2 R and u’s relative norm u(x)ū(x) in the index-2 subfield Q(ζN+ ζN-1), we can compute u(x) in poly-time. Corollary: Set v(x) = u(x)/ū(x). We can compute v(x) given a basis of J = (v). We know v(x)’s relative norm equal 1. Dimension-Halving for Principal Ideal Lattices Attack given a basis of I = (u): First, compute v(x) = u(x)/ū(x). Given a basis {u(x)ri(x)} of I, multiply by 1+1/v(x) to get a basis {(u(x)+ ū(x))ri(x)} of K = (u(x)+ū(x)) over R. Intersect K’s lattice with subring R’ = Z[ζN+ ζN-1] to get a basis {(u(x)+ ū(x))si(x) : si(x) 2 R’} of K over R’. Apply lattice reduction to lattice {u(x)si(x) : si(x) 2 R’}, which has half the usual dimension. Summary We have a “noisy” cryptographic multilinear map that can be used to construct, for example, predicate encryption for arbitrarily complex circuits. Construction is similar to NTRU-based homomorphic encryption, but with an equality-testing parameter. Security is based on somewhat stronger computational assumptions than NTRU. But more cryptanalysis needs to be done! And more applications need to be found! Thank You! Questions? Getting rid of principal ideals? Maybe present attacks and then say we can use general ideals. Obfuscation Obfuscation: I give the cloud an “encrypted” program E(P). For any input x, cloud can compute E(P)(x) = P(x). Cloud learns “nothing” about P, except {xi,P(xi)}. Barak et al: “On the (Im)possibility of Obfuscating Programs” Difference between obfuscation and FHE: In FHE, cloud computes E(P(x)), and it can’t decrypt to get P(x). Other Apps of Bilinear Maps: ABE Attribute-Based Encryption for Simple Functions [Sahai-Waters ‘05]: a generalization of IBE. Setup(1λ): Authority generates MSK, MPK. KeyGen(MSK, attr2{0,1}s): Authority uses MSK to generate a key SKattr for user who has attributes attr. Encrypt(MPK,policy2{0,1}s, m): Generate ciphertext CT that can only be decrypted by SKattr’s such that attr satisfies policy. Decrypt(SKattr,policy,CT): Decrypt if attr satisfies policy. ABE schemes using bilinear maps are “weak”. They can only enforce simple policies that can be described by low-depth circuits. Predicate Encryption for Circuits: Sketch of Sahai-Waters Construction Picture of Yao garbled circuit Mention that Yao GC is a predicate encryption scheme, except that it doesn’t offer any resistance against collusions, which is a serious shortcoming in typical multi-user settings. Predicate Encryption for Circuits: Sketch of Sahai-Waters Construction Now describe Sahai Waters as a gate-by-gate garbling, where the value for ‘1’ is a function of the encrypter’s randomness s, and randomness rw for the wire that is embedded in the user’s key. Semantic Security of NTRU