Gröbner Bases of Structured Systems and their Applications in Cryptology Jean-Charles Faugère, Mohab Safey El Din Pierre-Jean Spaenlehauer UPMC CNRS INRIA Paris - Rocquencourt LIP6 SALSA team SCPQ Webinar 2012, 03/06 1/28 PJ Spaenlehauer Algebraic Cryptanalysis Crypto primitive 2/28 PJ Spaenlehauer Algebraic Cryptanalysis Crypto primitive modeling 2/28 PJ Spaenlehauer Algebraic Cryptanalysis Crypto primitive modeling System solving 2/28 PJ Spaenlehauer Algebraic Cryptanalysis Crypto primitive modeling System solving Issues Which algebraic modeling ? Tradeo between the degree of the equations/number of variables ? Solving tools: Gröbner bases ? SAT-solvers ? ... Structure ? 2/28 PJ Spaenlehauer Motivations: Algebraic Structures in Cryptology Where does the structure come from ? 3/28 PJ Spaenlehauer Motivations: Algebraic Structures in Cryptology Where does the structure come from ? Non-linearity → Security Sometimes bi(or multi)-linear (e.g. AES S-boxes: 3/28 x · y − 1 = 0 for x 6= 0). PJ Spaenlehauer Motivations: Algebraic Structures in Cryptology Where does the structure come from ? Non-linearity → Security Sometimes bi(or multi)-linear (e.g. AES S-boxes: x · y − 1 = 0 for x 6= 0). Asymmetric encryption/signature: trapdoor (e.g. HFE, Multi-HFE, McEliece). Reducing the key sizes is a common issue → 3/28 potential weaknesses due to the structure. PJ Spaenlehauer Motivations: Algebraic Structures in Cryptology Where does the structure come from ? Non-linearity → Security Sometimes bi(or multi)-linear (e.g. AES S-boxes: x · y − 1 = 0 for x 6= 0). Asymmetric encryption/signature: trapdoor (e.g. HFE, Multi-HFE, McEliece). Reducing the key sizes is a common issue → potential weaknesses due to the structure. Symmetries, invariants: invariance of the solutions under some transformations (e.g. MinRank). ... 3/28 PJ Spaenlehauer Motivations: Algebraic Structures in Cryptology Where does the structure come from ? Non-linearity → Security Sometimes bi(or multi)-linear (e.g. AES S-boxes: x · y − 1 = 0 for x 6= 0). Asymmetric encryption/signature: trapdoor (e.g. HFE, Multi-HFE, McEliece). Reducing the key sizes is a common issue → potential weaknesses due to the structure. Symmetries, invariants: invariance of the solutions under some transformations (e.g. MinRank). ... Impact on the solving process ? 3/28 PJ Spaenlehauer Motivations: Algebraic Structures in Cryptology Where does the structure come from ? Non-linearity → Security Sometimes bi(or multi)-linear (e.g. AES S-boxes: x · y − 1 = 0 for x 6= 0). Asymmetric encryption/signature: trapdoor (e.g. HFE, Multi-HFE, McEliece). Reducing the key sizes is a common issue → potential weaknesses due to the structure. Symmetries, invariants: invariance of the solutions under some transformations (e.g. MinRank). ... Impact on the solving process ? Complexity ? Dedicated algorithms ? 3/28 PJ Spaenlehauer Families of structured algebraic systems Multi-homogeneous systems McEliece PKC. MinRank authentication scheme. ... 4/28 PJ Spaenlehauer Families of structured algebraic systems Multi-homogeneous systems McEliece PKC. MinRank authentication scheme. ... Determinantal systems MinRank authentication scheme. Cryptosystems based on rank metric codes. Hidden Field Equations and variants. ... 4/28 PJ Spaenlehauer Families of structured algebraic systems Multi-homogeneous systems McEliece PKC. MinRank authentication scheme. ... Determinantal systems MinRank authentication scheme. Cryptosystems based on rank metric codes. Hidden Field Equations and variants. ... Systems invariant by symmetries Discrete log on elliptic and hyperelliptic curves. 4/28 PJ Spaenlehauer Outline 1 Polynomial System Solving using Gröbner Bases 2 Bilinear Systems and Application to McEliece 3 Determinantal Systems and Applications to MinRank and HFE 5/28 PJ Spaenlehauer Gröbner bases (I) Gröbner bases I a polynomial ideal. Gröbner basis (w.r.t. a monomial ordering): of polynomials such that LM(I) = hLM(G )i. Buchberger [Buchberger Ph.D. 65]. F4 [Faugère J. of Pure and Appl. Alg. 99]. F5 [Faugère ISSAC'02]. FGLM [Faugère/Gianni/Lazard/Mora JSC. 93, 6/28 PJ Spaenlehauer Faugère/Mou G ⊂ I a nite set ISSAC'11]. Gröbner bases (I) Gröbner bases I a polynomial ideal. Gröbner basis (w.r.t. a monomial ordering): of polynomials such that LM(I) = hLM(G )i. Buchberger [Buchberger Ph.D. 65]. F4 [Faugère J. of Pure and Appl. Alg. 99]. F5 [Faugère ISSAC'02]. FGLM [Faugère/Gianni/Lazard/Mora JSC. 93, 0-dimensional ⊂ I a nite set ISSAC'11]. system solving Polynomial system 6/28 Faugère/Mou G F4 /F5 −−−−→ grevlex GB PJ Spaenlehauer FGLM −−−−→ lex GB. Gröbner bases (I) Gröbner bases I a polynomial ideal. Gröbner basis (w.r.t. a monomial ordering): of polynomials such that LM(I) = hLM(G )i. Buchberger [Buchberger Ph.D. 65]. F4 [Faugère J. of Pure and Appl. Alg. 99]. F5 [Faugère ISSAC'02]. FGLM [Faugère/Gianni/Lazard/Mora JSC. 93, 0-dimensional Faugère/Mou G ⊂ I a nite set ISSAC'11]. system solving Polynomial system F4 /F5 −−−−→ grevlex GB FGLM −−−−→ lex GB. XL/MXL Most of the complexity results also valid for XL/MXL Buchman/Bulygin/Cabarcas/Ding/Mohamed/Mohamed PQCrypto 2008, Africacrypt 2010,. . . Ars/Faugère/Imai/Kawazoe/Sugita, Asiacrypt 2004 Albrecht/Cid/Faugère/Perret, eprint 6/28 PJ Spaenlehauer Gröbner bases (II) 0-dimensional system solving Polynomial system F4 /F5 −−−−→ grevlex GB Lexicographical Gröbner basis of 0-dimensional systems Equivalent system in triangular shape: 7/28 ( , . . . , xn ) = 0 ( , . . . , xn ) ( , . . . , xn ) = = 0 0 = = 0 0 f1 x1 f` x1 f`+1 x2 fm−1 xn−1 xn ( , ) ( ) fm xn .. . .. . PJ Spaenlehauer FGLM −−−−→ lex GB. Gröbner bases (II) 0-dimensional system solving Polynomial system F4 /F5 −−−−→ grevlex GB FGLM −−−−→ lex GB. Lexicographical Gröbner basis of 0-dimensional systems Equivalent system in triangular shape: 7/28 ( , . . . , xn ) = 0 ( , . . . , xn ) ( , . . . , xn ) = = 0 0 = = 0 0 f1 x1 f` x1 f`+1 x2 fm−1 xn−1 xn ( , ) ( ) fm xn .. . .. . =⇒ Find the roots of univariate polynomials → easy in nite elds. PJ Spaenlehauer Macaulay matrix in degree d I = hf1 , . . . , fp i deg(fi ) = di a monomial ordering Rows: all products tfi where t is a monomial of degree at most Columns: monomials of degree at most d. t1 f1 .. . tk fp d m1 · · · m` row echelon form of the Macaulay matrix with d suciently high =⇒ 8/28 Gröbner basis. PJ Spaenlehauer − di . Macaulay matrix in degree d I = hf1 , . . . , fp i deg(fi ) = di a monomial ordering Rows: all products tfi where t is a monomial of degree at most Columns: monomials of degree at most d. t1 f1 .. . tk fp d − di . m1 · · · m` row echelon form of the Macaulay matrix with d suciently high =⇒ Gröbner basis. Problems Degree falls. Rank defect useless computations. Hilbert series: generating series of the rank defects of the Macaulay matrices. Which d ? degree of regularity. 8/28 PJ Spaenlehauer Complexity of Gröbner bases computations Two main indicators of the complexity dreg degree that has to be reached to compute the Degree of regularity Degree of the ideal I = hf1 , . . . fm i Number of solutions the Macaulay matrix. 9/28 grevlex GB. of the system (counted with multiplicities). Gives the PJ Spaenlehauer rank of Complexity of Gröbner bases computations Two main indicators of the complexity dreg degree that has to be reached to compute the Degree of regularity Degree of the ideal Number of solutions the Macaulay matrix. of the system (counted with multiplicities). Gives the −→ System Algorithms Complexity 9/28 grevlex GB. I = hf1 , . . . fm i O n + dreg ω −→ grevlex GB grevlex GB Change of Ordering dreg PJ Spaenlehauer O (n · #Solω ) rank lex GB. of Complexity of Gröbner bases computations Two main indicators of the complexity dreg degree that has to be reached to compute the Degree of regularity Degree of the ideal Number of solutions the Macaulay matrix. of the system (counted with multiplicities). Gives the −→ System Algorithms O n + dreg ω Change of Ordering O dreg Classical bounds (sharp for generic systems) Let f1 , . . . , fn ∈ K[x1 , . . . , xn ] be a "generic" system. Macaulay bound: Bézout bound: 9/28 dreg ≤ 1 + X 1≤i ≤n deg(hf1 , . . . , fn i) ≤ −→ grevlex GB grevlex GB Complexity grevlex GB. I = hf1 , . . . fm i (di − 1). Y 1≤i ≤n i. d PJ Spaenlehauer (n · #Solω ) rank lex GB. of Complexity of Gröbner bases computations Two main indicators of the complexity dreg degree that has to be reached to compute the Degree of regularity Degree of the ideal Number of solutions the Macaulay matrix. of the system (counted with multiplicities). Gives the −→ System Algorithms O n −→ grevlex GB grevlex GB Complexity grevlex GB. I = hf1 , . . . fm i + dreg ω Change of Ordering O dreg (n · #Solω ) Classical bounds (sharp for generic systems) Let f1 , . . . , fn ∈ K[x1 , . . . , xn ] be a "generic" system. Macaulay bound: Bézout bound: 9/28 dreg ≤ 1 + X 1≤i ≤n deg(hf1 , . . . , fn i) ≤ (di − 1). Y 1≤i ≤n i. d Are there sharper bounds for structured systems ? PJ Spaenlehauer rank lex GB. of Plan 1 Polynomial System Solving using Gröbner Bases 2 Bilinear Systems and Application to McEliece 3 Determinantal Systems and Applications to MinRank and HFE 10/28 PJ Spaenlehauer Multi-homogeneous systems Multi-homogeneous polynomial ∈ K[X (1) , . . . , X (`) ] is multi-homogeneous of multi-degree (d1 , . . . , d` ) if for all λ1 , . . . , λ` , f f 11/28 d (λ1 X (1) , . . . , λ` X (`) ) = λd11 . . . λ` ` f (X (1) , . . . , X (`) ). PJ Spaenlehauer Multi-homogeneous systems Multi-homogeneous polynomial ∈ K[X (1) , . . . , X (`) ] is multi-homogeneous of multi-degree (d1 , . . . , d` ) if for all λ1 , . . . , λ` , f f d (λ1 X (1) , . . . , λ` X (`) ) = λd11 . . . λ` ` f (X (1) , . . . , X (`) ). Example: 3x12 y1 + 4x1 x2 y1 − 3x22 y1 − x12 y2 + 8x1 x2 y2 − 5x22 y2 + 10x12 y3 − 2x1 x2 y3 − 3x22 y3 is a bi-homogeneous polynomial of bi-degree (2, 1) in F11 [x1 , x2 , y1 , y2 , y3 ]. 11/28 PJ Spaenlehauer Multi-homogeneous systems Multi-homogeneous polynomial ∈ K[X (1) , . . . , X (`) ] is multi-homogeneous of multi-degree (d1 , . . . , d` ) if for all λ1 , . . . , λ` , f f d (λ1 X (1) , . . . , λ` X (`) ) = λd11 . . . λ` ` f (X (1) , . . . , X (`) ). Example: 3x12 y1 + 4x1 x2 y1 − 3x22 y1 − x12 y2 + 8x1 x2 y2 − 5x22 y2 + 10x12 y3 − 2x1 x2 y3 − 3x22 y3 is a bi-homogeneous polynomial of bi-degree (2, 1) in F11 [x1 , x2 , y1 , y2 , y3 ]. Bilinear system: multi-homogeneous of multi-degree (1, 1) f1 , . . . , fq ∈ K[X , Y ]: bilinear forms. fk 11/28 = P a (k ) , xi yj . i j PJ Spaenlehauer Structure of bilinear systems Euler relations f1 12/28 , . . . , fq ∈ K[X , Y ]: bilinear forms. P (k ) fk = a i ,j xi yj . PJ Spaenlehauer Structure of bilinear systems Euler relations f1 , . . . , fq ∈ K[X , Y ]: bilinear forms. P (k ) fk = a i ,j xi yj . fk 12/28 = X ∂ fk X ∂ fk xi = yj . ∂ xi ∂ yj i PJ Spaenlehauer j Structure of bilinear systems Euler relations f1 , . . . , fq ∈ K[X , Y ]: bilinear forms. P (k ) fk = a i ,j xi yj . fk ∂ f1 ∂ x1 . . jacx (F ) = . ∂ fq ∂ x1 f1 = X ∂ fk X ∂ fk xi = yj . ∂ xi ∂ yj i ... ∂ f1 ∂ xnx ... ∂ fq ∂ xnx .. . j 12/28 ∂ y1 .. . =⇒ ... = jacx (F ) · fq ∂ f1 . . jacy (F ) = . ∂ fq ∂ y1 x1 . . . .. . PJ Spaenlehauer .. . . ... ∂ fq ∂ yny = jacy (F ) · xnx ∂ f1 ∂ yny ... y1 . . . yny . Something special happens with minors... f1 x1 .. . . = jacx (F ) · .. . fq xnx If (x1 , . . . , xn , y1 , . . . , yn ) is a non-trivial solution of F , then jacx (F ) is rank defective. (y1 , . . . , yn ) is a zero of the maximal minors of jacx (F ). x y y 13/28 PJ Spaenlehauer Something special happens with minors... f1 x1 .. . . = jacx (F ) · .. . fq xnx If (x1 , . . . , xn , y1 , . . . , yn ) is a non-trivial solution of F , then jacx (F ) is rank defective. (y1 , . . . , yn ) is a zero of the maximal minors of jacx (F ). x y y Bernstein/Sturmfels/Zelevinski, Adv. in Math. 1993 a p × q matrix whose entries are variables. For any monomial ordering, the maximal minors of M are a Gröbner basis of the associated ideal. M 13/28 PJ Spaenlehauer Something special happens with minors... f1 x1 .. . . = jacx (F ) · .. . fq xnx If (x1 , . . . , xn , y1 , . . . , yn ) is a non-trivial solution of F , then jacx (F ) is rank defective. (y1 , . . . , yn ) is a zero of the maximal minors of jacx (F ). x y y Bernstein/Sturmfels/Zelevinski, Adv. in Math. 1993 a p × q matrix whose entries are variables. For any monomial ordering, the maximal minors of M are a Gröbner basis of the associated ideal. M Faugère/Safey El Din/S., J. of Symb. Comp. 2011 a k -variate q × p linear matrix (with q > p ). Generically, a grevlex GB of hMinors(M )i: linear combination of the generators. M dreg (MaxMinors(jacx (F ))) 13/28 PJ Spaenlehauer = nx . Complexity Ane bilinear polynomial ∈ K[x1 , . . . , xn , y1 , . . . , yn ] is said to be ane bilinear if there exists a bilinear polynomial f˜ in K[x0 , . . . , xn , y0 , . . . , yn ] such that f x y x f y (x1 , . . . , xn , y1 , . . . , yn ) = f˜(1, x1 , . . . , xn , 1, y1 , . . . , yn ). x y x y Faugère/Safey El Din/S., J. of Symb. Comp. 2011 Degree of regularity Let f1 , . . . , fn +n be an ane bilinear system in K[x1 , . . . , xn , y1 , . . . , yn ]. Then the highest degree reached during the computation of a Gröbner basis for the grevlex x y x ordering is upper bounded by min(nx , ny ) + 1 nx + ny + 1. 14/28 PJ Spaenlehauer y Complexity Ane bilinear polynomial ∈ K[x1 , . . . , xn , y1 , . . . , yn ] is said to be ane bilinear if there exists a bilinear polynomial f˜ in K[x0 , . . . , xn , y0 , . . . , yn ] such that f x y x f y (x1 , . . . , xn , y1 , . . . , yn ) = f˜(1, x1 , . . . , xn , 1, y1 , . . . , yn ). x y x y Faugère/Safey El Din/S., J. of Symb. Comp. 2011 Degree of regularity Let f1 , . . . , fn +n be an ane bilinear system in K[x1 , . . . , xn , y1 , . . . , yn ]. Then the highest degree reached during the computation of a Gröbner basis for the grevlex x y x y ordering is upper bounded by min(nx , ny ) + 1 nx + ny + 1. Consequences The complexity of computing a grevlex GB is polynomial in the number of solutions !! Bilinear systems with unbalanced sizes of blocks of variables are easy to solve !! 14/28 PJ Spaenlehauer Modeling of McEliece cryptosystem Based on alternant codes: secret key: a parity-check matrix of the form H = y0 y1 y0 x0 y1 x1 .. . t −1 y0 x0 .. . t −1 y1 x1 ... ... .. . ... yn−1 yn−1 xn−1 .. . t −1 , yn xn where xi , yj ∈ F2 , with x0 , . . . , xn pairwise distinct and yj 6= 0. public key: a generator matrix G of the same code. m 15/28 PJ Spaenlehauer Modeling of McEliece cryptosystem Based on alternant codes: secret key: a parity-check matrix of the form H = y0 y1 y0 x0 y1 x1 .. . t −1 y0 x0 .. . t −1 y1 x1 ... ... .. . ... yn−1 yn−1 xn−1 .. . t −1 , yn xn where xi , yj ∈ F2 , with x0 , . . . , xn pairwise distinct and yj 6= 0. public key: a generator matrix G of the same code. m Problem Given 15/28 G , nd H such that H · Gt = 0 ! PJ Spaenlehauer Modeling of McEliece cryptosystem Based on alternant codes: secret key: a parity-check matrix of the form H = y0 y1 y0 x0 y1 x1 .. . .. . t −1 t −1 y0 x0 y1 x1 ... ... .. yn−1 yn−1 xn−1 .. . . ... t −1 , yn xn where xi , yj ∈ F2 , with x0 , . . . , xn pairwise distinct and yj 6= 0. public key: a generator matrix G of the same code. m Problem Given G , nd H such that H ∀i , j , · Gt = 0 ! j gi ,0 y0 x0 + · · · + gi ,n−1 yn−1 xnj −1 = 0. ⇒ Bi-homogeneous structure !! 15/28 PJ Spaenlehauer Cryptanalysis of compact variants of McEliece Compact variants Goal: reduce the size of the keys. Quasi-cyclic variant: Berger/Cayrel/Gaborit/Otmani Africacrypt'09; Dyadic variant: Misoczy/Barreto SAC'09. 16/28 PJ Spaenlehauer Cryptanalysis of compact variants of McEliece Compact variants Goal: reduce the size of the keys. Quasi-cyclic variant: Berger/Cayrel/Gaborit/Otmani Africacrypt'09; Dyadic variant: Misoczy/Barreto SAC'09. Faugère/Otmani/Perret/Tilich, Eurocrypt'2010 ⇒ add redundancy to the polynomial system linear equations 16/28 less variables. PJ Spaenlehauer Cryptanalysis of compact variants of McEliece Compact variants Goal: reduce the size of the keys. Quasi-cyclic variant: Berger/Cayrel/Gaborit/Otmani Africacrypt'09; Dyadic variant: Misoczy/Barreto SAC'09. Faugère/Otmani/Perret/Tilich, Eurocrypt'2010 ⇒ add redundancy to the polynomial system linear equations less variables. Moreover, the system is still over-determined and one can extract a subsystem containing only powers of two: ∀i , j a power of two !!, 16/28 j gi ,0 y0 x0 + · · · + gi ,n−1 yn−1 xnj −1 = 0. PJ Spaenlehauer Cryptanalysis of compact variants of McEliece Compact variants Goal: reduce the size of the keys. Quasi-cyclic variant: Berger/Cayrel/Gaborit/Otmani Africacrypt'09; Dyadic variant: Misoczy/Barreto SAC'09. Faugère/Otmani/Perret/Tilich, Eurocrypt'2010 ⇒ add redundancy to the polynomial system linear equations less variables. Moreover, the system is still over-determined and one can extract a subsystem containing only powers of two: ∀i , j a power of two !!, j gi ,0 y0 x0 + · · · + gi ,n−1 yn−1 xnj −1 = 0. Decomposing the subsystem over the eld F2 ⇒ 16/28 Bilinear system with PJ Spaenlehauer nx ny !!! Cryptanalysis of compact variants of McEliece Compact variants Goal: reduce the size of the keys. Quasi-cyclic variant: Berger/Cayrel/Gaborit/Otmani Africacrypt'09; Dyadic variant: Misoczy/Barreto SAC'09. Faugère/Otmani/Perret/Tilich, Eurocrypt'2010 ⇒ add redundancy to the polynomial system linear equations less variables. Moreover, the system is still over-determined and one can extract a subsystem containing only powers of two: ∀i , j a power of two !!, j gi ,0 y0 x0 + · · · + gi ,n−1 yn−1 xnj −1 = 0. Decomposing the subsystem over the eld F2 ⇒ Bilinear system with nx ny !!! Theoretical and Practical attacks on the quasi-cyclic and dyadic variants of McEliece !! 16/28 PJ Spaenlehauer Plan 1 Polynomial System Solving using Gröbner Bases 2 Bilinear Systems and Application to McEliece 3 Determinantal Systems and Applications to MinRank and HFE 17/28 PJ Spaenlehauer The r ∈ N. MinRank M0 , . . . , Mk : MinRank k problem + 1 matrices of size m × m. nd λ1 , . . . , λk such that Rank M0 − k X i =1 ! λi Mi ≤ r. Multivariate generalization of the Eigenvalue problem. Applications in cryptology, coding theory, ... Crypto'99, Courtois Asiacrypt'01 Crypto'08,... Fundamental NP-hard problem of linear algebra. Kipnis/Shamir Faugère/Levy-dit-Vehel/Perret Buss, Frandsen, Shallit. The computational complexity of some problems of linear algebra. 18/28 PJ Spaenlehauer Two algebraic modelings M = M0 − 19/28 k X i =1 λi Mi . PJ Spaenlehauer Two algebraic modelings M = M0 − k X i =1 λi Mi . The minors modeling Rank(M) ≤r m all minors of size (r + 1) of M vanish. m 2 r +1 k equations of degree r + 1. variables. Few variables, lots of equations, high degree !! 19/28 PJ Spaenlehauer Two algebraic modelings M = M0 − k X i =1 λi Mi . The Kipnis-Shamir modeling The minors modeling Rank(M) ≤r m all minors of size (r + 1) of M vanish. m 2 r +1 equations of degree k r + 1. M variables. Few variables, lots of equations, high degree !! 19/28 ≤ r ⇔ ∃x (1) , . . . , x (m−r ) ∈ Ker(M). I − (1) (m−r ) x . . . x1 · 1 . . . . . . . . . . (1) (m−r ) xr . . . xr Rank(M) ( m m k PJ Spaenlehauer m r =0 − r ) bilinear equations. + r (m − r ) variables. Two algebraic modelings M = M0 − k X i =1 λi Mi . The Kipnis-Shamir modeling The minors modeling Rank(M) ≤r m all minors of size (r + 1) of M vanish. m 2 r +1 equations of degree k ≤ r ⇔ ∃x (1) , . . . , x (m−r ) ∈ Ker(M). I − (1) (m−r ) x . . . x1 · 1 . . . . . . . . . . (1) (m−r ) xr . . . xr Rank(M) r + 1. M variables. Few variables, lots of equations, high degree !! ( m m k m r =0 − r ) bilinear equations. + r (m − r ) variables. Complexity of solving MinRank using Gröbner bases techniques ? Comparison of the two modelings ? Number of solutions ? 19/28 PJ Spaenlehauer Main results System Algorithms Complexity 20/28 O −→ −→ grevlex GB GB grevlex n + d ω reg dreg PJ Spaenlehauer Change of Ordering O (n · #Solω ) lex GB. Main results −→ System Complexity O n −→ grevlex GB grevlex GB Algorithms + dreg ω lex GB. Change of Ordering O dreg (n · #Solω ) m: size of the matrices, k: number of matrices, r: target rank. k = (m − r)2 . Modeling: Degree of regularity when k Minors Macaulay bound: ≤ m (m − r ) + 1 = ( m − r )2 # Sol MH. Bézout: ≤ Complexity 20/28 Kipnis-Shamir PJ Spaenlehauer m r !m−r Main results −→ System Complexity O n −→ grevlex GB grevlex GB Algorithms lex GB. Change of Ordering + dreg ω O dreg (n · #Solω ) m: size of the matrices, k: number of matrices, r: target rank. k = (m − r)2 . Modeling: Degree of regularity when k Minors = (m − r )2 # Sol ≤ (m − r )2 + 1 !m−r MH. Bézout: ≤ Complexity 20/28 Kipnis-Shamir PJ Spaenlehauer m r Main results −→ System Complexity O n −→ grevlex GB grevlex GB Algorithms lex GB. Change of Ordering + dreg ω O dreg (n · #Solω ) m: size of the matrices, k: number of matrices, r: target rank. k = (m − r)2 . Modeling: Degree of regularity when k = (m − r )2 # Sol Minors ( r m − r) + 1 ≤ (m − r )2 + 1 !m−r MH. Bézout: ≤ Complexity 20/28 Kipnis-Shamir PJ Spaenlehauer m r Main results −→ System Complexity O n −→ grevlex GB grevlex GB Algorithms lex GB. Change of Ordering + dreg ω O dreg (n · #Solω ) m: size of the matrices, k: number of matrices, r: target rank. k = (m − r)2 . Modeling: Degree of regularity when k = (m − r )2 # Sol Complexity 20/28 Minors ( r m − r) + 1 m− r −1 Y i =0 Kipnis-Shamir ≤ (m − r )2 + 1 i !(m + i )! (m − 1 − i )!(m − r + i )! PJ Spaenlehauer Main results −→ System Complexity O n −→ grevlex GB grevlex GB Algorithms lex GB. Change of Ordering + dreg ω O dreg (n · #Solω ) m: size of the matrices, k: number of matrices, r: target rank. k = (m − r)2 . Modeling: Degree of regularity when k = (m − r )2 # Sol Complexity 20/28 Minors ( r m − r) + 1 m− r −1 Y i =0 ( O m Kipnis-Shamir ≤ (m − r )2 + 1 i !(m + i )! (m − 1 − i )!(m − r + i )! ωk PJ Spaenlehauer ) ( O m ω(k +1) ) Main results −→ System Complexity O n −→ grevlex GB grevlex GB Algorithms lex GB. Change of Ordering + dreg ω O dreg (n · #Solω ) m: size of the matrices, k: number of matrices, r: target rank. k = (m − r)2 . Modeling: Degree of regularity when k = (m − r )2 # Sol Complexity Minors ( r m − r) + 1 m− r −1 Y i =0 ( O m Kipnis-Shamir ≤ (m − r )2 + 1 i !(m + i )! (m − 1 − i )!(m − r + i )! ωk ) ( O m ω(k +1) ) Both modelings → polynomial complexity when k = (m − r)2 is xed. New Crypto challenge broken: 10 generic matrices of size 11 × 11 target rank 8, K = GF(65521). Courtois, Asiacrypt 2001. 20/28 PJ Spaenlehauer Minors modeling Minors modeling: Rank(M) ≤r m all minors of size (r + 1) of M vanish. Determinantal ideal 21/28 PJ Spaenlehauer Minors modeling Minors modeling: Rank(M) ≤r m all minors of size (r + 1) of M vanish. Determinantal ideal Bilinear systems ↔ determinantal systems f1 , . . . , fq ∈ K[X , Y ]: bilinear forms. ∂ f1 ∂ x0 . . . ∂ fq ∂ x0 f1 21/28 ... .. . ... ∂ f1 ∂ xnx x0 f1 .. .. .. · = . . . ∂ fq ∂ xnx xnx fq = . . . = fq = 0 ⇐⇒ MaxMinors (JacX (f1 , . . . , fq )) = 0. PJ Spaenlehauer Determinantal ideals What is known Determinantal ideals: Bernstein/Zelevinsky J. of Alg. Comb. 93, Bruns/Conca 98, Sturmfels/Zelevinsky Adv. Math. 98, Conca/Herzog AMS'94, Lascoux 78, Abhyankar 88... Geometry of determinantal varieties: Room 39, Fulton Duke Math. J. 91, Giusti/Merle Int. Conf. on Alg. Geo. 82... Polar varieties: Bank/Giusti/Heintz/Safey/Schost AAECC'10,Bank/Giusti/Heintz/Pardo J. of Compl. 05, Safey/Schost ISSAC'03, Teissier Pure and Appl. Math. 91... 22/28 PJ Spaenlehauer Properties of Determinantal Ideals v1,1 . D = Minorsr +1 .. v m ,1 m ... .. v1, .. . vm,m . ... Thom, Porteous, Giambelli, Harris-Tu, ... The degree m− r −1 Y i =0 of D is i !(m + i )! . (m − 1 − i )!(m − r + i )! Conca/Herzog, Abhyankar The Hilbert series of D is det(A(t )) HSD (t ) = r t 2 (1 − t )(2m−r )r A . i ,j (t ) = X m − i m − j ` 23/28 ` PJ Spaenlehauer ` t ` . Properties of Determinantal Ideals v1,1 . D = Minorsr +1 .. v m ,1 m ... .. v1, .. . . ... v degree m− r −1 Y i =0 m,m of D is i !(m + i )! . (m − 1 − i )!(m − r + i )! Hilbert series of D is det(A(t )) HSD (t ) = r t 2 (1 − t )(2m−r )r A . i ,j (t ) = X m − i m − j ` 23/28 m,1 ... f Conca/Herzog, Abhyankar The ... . I = Minorsr +1 .. Thom, Porteous, Giambelli, Harris-Tu, ... The f1,1 ` PJ Spaenlehauer ` t ` . .. . m .. . fm,m f1, Properties of Determinantal Ideals v1,1 . D = Minorsr +1 .. v m ,1 m ... .. v1, .. . . ... v f1,1 ... m,1 ... . I = Minorsr +1 .. m,m f .. . Thom, Porteous, Giambelli, Harris-Tu, ... The degree m− r −1 Y i =0 of D is i !(m + i )! . (m − 1 − i )!(m − r + i )! Conca/Herzog, Abhyankar The Hilbert series of D is det(A(t )) HSD (t ) = r t 2 (1 − t )(2m−r )r A . i ,j (t ) = X m − i m − j ` transfer of properties of 23/28 t ` . ` ` D by adding PJ Spaenlehauer hvi ,j − fi ,j i m .. . fm,m f1, Properties of Determinantal Ideals v1,1 . D = Minorsr +1 .. v m ,1 m ... .. v1, . ... v degree m− r −1 Y i =0 of D is m− r −1 Y !(m + i )! . (m − 1 − i )!(m − r + i )! i =0 ISSAC'2010 The Hilbert of D is det(A(t )) HSD (t ) = r t 2 (1 − t )(2m−r )r A series X m − i m − j transfer of properties of . t ` det(A(t )) r 2 (1 − t )k −(m−r ) . ` ` D by adding PJ Spaenlehauer of I is t ` 23/28 ... i !(m + i )! . (m − 1 − i )!(m − r + i )! HSI (t ) = . i ,j (t ) = m,1 .. m .. . fm,m f1, ISSAC'2010 The degree of I is i Hilbert series ... f Conca/Herzog, Abhyankar The f1,1 . I = Minorsr +1 .. m,m Thom, Porteous, Giambelli, Harris-Tu, ... The .. . hvi ,j − fi ,j i 2 . Complexity of the minors formulation (ISSAC'2010) Degree of regularity for a 0-dim ideal = 1+ degree of the Hilbert series. Corollary The degree of regularity of I is generically equal to dreg = r (m − r ) + 1. 24/28 PJ Spaenlehauer Complexity of the minors formulation (ISSAC'2010) Degree of regularity for a 0-dim ideal = 1+ degree of the Hilbert series. Corollary The degree of regularity of I is generically equal to dreg = r (m − r ) + 1. Number of matrices and rank defect xed. 0-dimensional case. Corollary: asymptotic complexity When k = (m − r )2 is xed, then the complexity of the Gröbner basis computation of the minors modeling is O mω 24/28 k PJ Spaenlehauer . Complexity of the Change of Ordering Corollary: generic number of solutions The number of solutions of a generic MinRank problem with k = (m − r )2 is #Sol = ∼ m→∞ 25/28 m− r −1 Y i !(m + i )! (m − 1 − i )!(m − r + i )! i =0 m− r −1 Y i! k . m (m − r + i )! i =0 PJ Spaenlehauer Complexity of the Change of Ordering Corollary: generic number of solutions The number of solutions of a generic MinRank problem with k = (m − r )2 is #Sol = ∼ m→∞ m− r −1 Y i !(m + i )! (m − 1 − i )!(m − r + i )! i =0 m− r −1 Y i! k . m (m − r + i )! i =0 Complexity of the Change of Ordering (ISSAC 2010) The complexity of FGLM is upper bounded by O (#Sol ω ) . If k = (m − r )2 , then O 25/28 (#Sol ω ) = O PJ Spaenlehauer m ωk . Experimental results Courtois. Asiacrypt'01. Ecient zero-knowledge authentication based on a linear algebra problem MinRank. K = GF(65521) (m, k, r): k matrices of size m × m. Target rank: r . Challenge degree dreg time F5 mem log2 (Nb op.) FGLM time F5 dreg time F5 mem log2 (Nb op.) FGLM time F5 26/28 A B (6, 9, 3) (7, 9, 4) 980 4116 (8, 9, 5) (9, 9, 6) 14112 41580 Minors modeling 10 13 16 19 1.1s 28.4s 544s 9048s 488 MB 587 MB 1213 MB 5048 MB 21.5 25.9 29.2 32.7 0.5s 28.5s 1033s 22171s Kipnis-Shamir modeling 5 6 30s 3795s 328233s 7 407 MB 3113 MB 58587 MB 30.5 37.1 43.4 35s 2580s ∞ PJ Spaenlehauer ∞ C (11, 9, 8) 259545 - Experimental results Courtois. Asiacrypt'01. Ecient zero-knowledge authentication based on a linear algebra problem MinRank. K = GF(65521) (m, k, r): k matrices of size m × m. Target rank: r . Challenge degree dreg time F5 mem log2 (Nb op.) FGLM time F5 dreg time F5 mem log2 (Nb op.) FGLM time F5 26/28 A B (6, 9, 3) (7, 9, 4) 980 4116 (8, 9, 5) (9, 9, 6) 14112 41580 Minors modeling 10 13 16 19 1.1s 28.4s 544s 9048s 488 MB 587 MB 1213 MB 5048 MB 21.5 25.9 29.2 32.7 0.5s 28.5s 1033s 22171s Kipnis-Shamir modeling 5 6 30s 3795s 328233s 7 407 MB 3113 MB 58587 MB 30.5 37.1 43.4 35s 2580s ∞ ∞ Computational bottleneck: computing the minors. Computing eort needed for solving Challenge C: 238 days on 64 quadricore processors. PJ Spaenlehauer C (11, 9, 8) 259545 - Algebraic cryptanalysis of (multi-)HFE Patarin, Eurocrypt'96 Billet/Patarin/Seurin, ICSCC'08 Ding/Schmitt/Werner, Information Security, 2008 ( )= P x X 0≤i ,j ≤r pi ,j x q +q i j ∈ Fq , with low-rank quadratic form (Fq )n → (Fq )n 27/28 PJ Spaenlehauer n r n Algebraic cryptanalysis of (multi-)HFE Patarin, Eurocrypt'96 Billet/Patarin/Seurin, ICSCC'08 Ding/Schmitt/Werner, Information Security, 2008 X ( )= P x 0≤i ,j ≤r pi ,j x q +q i j ∈ Fq , with low-rank quadratic form (Fq )n → (Fq )n masked by linear transforms !! 27/28 PJ Spaenlehauer n r n Algebraic cryptanalysis of (multi-)HFE Patarin, Eurocrypt'96 Billet/Patarin/Seurin, ICSCC'08 Ding/Schmitt/Werner, Information Security, 2008 X ( )= P x 0≤i ,j ≤r pi ,j x q +q i j ∈ Fq , with n r n low-rank quadratic form (Fq )n → (Fq )n masked by linear transforms !! ⇒ the secret polynomial can be recovered by solving a MinRank problem. Bettale/Faugère/Perret, PKC 2011 The complexity of solving this MinRank problem is upper bounded by O n (r +1)ω . algebraic attack with polynomial complexity in n !! attacks on odd-characteristic variants; generalizations to multi-HFE. 27/28 PJ Spaenlehauer Conclusion and Perspectives Structures have an impact on the complexity of the solving process in algebraic cryptanalysis ! Structure Design, key size reduction,. . . ←→ potential algebraic attacks. 28/28 PJ Spaenlehauer Conclusion and Perspectives Structures have an impact on the complexity of the solving process in algebraic cryptanalysis ! Structure Design, key size reduction,. . . ←→ potential algebraic attacks. Other possible applications in Crypto of structured systems Rank metric codes (Gabidulin/Ourivski/Honary/Ammar classical McEliece PKC (McEliece 1978 ). 28/28 PJ Spaenlehauer IEEE IT, 2003 ). Conclusion and Perspectives Structures have an impact on the complexity of the solving process in algebraic cryptanalysis ! Structure Design, key size reduction,. . . ←→ potential algebraic attacks. Other possible applications in Crypto of structured systems Rank metric codes (Gabidulin/Ourivski/Honary/Ammar classical McEliece PKC (McEliece 1978 ). Algorithmic problems Dedicated F5 algorithm for multi-homogeneous systems. (Faugère, Safey, S., J. of Symb. Comp. 2011 ) Dedicated algorithm for determinantal systems ? 28/28 PJ Spaenlehauer IEEE IT, 2003 ).