1. Secure Computation (Lecture 5) Arpita Patra

2. Recap >> Scope of MPC > models of computation > network models > modelling distrust (centralized/decentralized adversary) > modelling adversary > Various Parameters/questions asked in MPC >> Defining Security of MPC > Ideal World & Real world > Indistinguishability of the view of adversary in real and ideal (with the help of simulator) world > Indistinguishability of the joint dist. of the output of the honest parties and the view of adversary in real and ideal (with the help of simulator) world.

3. Ideal World MPC x1x1 x2x2 x3x3 x4x4 Any task (y 1,y 2,y 3,y 4 ) = f(x 1,x 2,x 3,x 4 )

4. Ideal World MPC Any task y1y1 y2y2 y4y4 y3y3 The Ideal World y1y1 y2y2 y4y4 y3y3 The Real World (y 1,y 2,y 3,y 4 ) = f(x 1,x 2,x 3,x 4 ) x1x1 x2x2 x3x3 x4x4 x1x1 x2x2 x3x3 x4x4

5. How do you compare Real world with Ideal World? >> Fix the inputs of the parties, say x 1,….x n >> Real world view of adv contains no more info than ideal world view View Real i : The view of P i on input (x 1,….x n ) - Leaked Values {x 3, y 3, r 3, protocol transcript} The Real World y1y1 y2y2 y4y4 {View Real i } Pi in C {x 3, y 3 } y1y1 y2y2 y4y4 The Ideal World View Ideal i : The view of P i on input (x 1,….x n ) - Allowed values {View Ideal i } Pi in C Our protocol is secure if the leaked values contains no more info than allowed values

6. Real world (leaked values) vs. Ideal world (allowed values) {x 3, y 3, r 3, protocol transcript} The Real World y1y1 y2y2 y4y4 {x 3, y 3 } y1y1 y2y2 y4y4 The Ideal World >> If leaked values can be efficiently computed from allowed values. >> Such an algorithm is called SIMULATOR (simulates the view of the adversary in the real protocol). >> It is enough if SIM creates a view of the adversary is “close enough” to the real view so that adv. can not distinguish from its real view.

7. Definition1: View indistinguishability of Adversary in Real and Ideal world {View Real i } Pi in C The Real World y1y1 y2y2 y4y4 {x 3, y 3 } y1y1 y2y2 y4y4 The Ideal World SIM Interaction on behalf of the honest parties SIM: Ideal Adversary {View Ideal i } Pi in C  Random Variable/distribution (over the random coins of parties) Random Variable/distribution (over the random coins of SIM and adv) {x 3, y 3, r 3, protocol transcript}

8. Definition 2: Indistinguishability of Joint Distributions of Output and View >> Joint distribution of output & view of the honest & corrupted parties in both the worlds can not be told apart Output Real i : The output of P i on input (x 1,….x n ) when P i is honest. View Real i : As defined before when P i is corrupted. The Real World [ {View Real i } Pi in C, {Output Real i } Pi in H ] The Ideal World Output Ideal i : The output of P i on input (x 1,….x n ) when P i is honest. View Ideal i : As defined before when P i is corrupted. [ {View Ideal i } Pi in C, {Output Ideal i } Pi in H ]  >> First note that this def. subsumes the previous definition (stronger) >> Captures randomized functions as well

9. Randomized Function and Definition 1 The Real WorldThe Ideal World >> Is this protocol secure? The proof says the protocol is secure! f(, ) = (r, ) r is a random bit r... Sample r randomly.. Sample r randomly and output r No! {View Real i } Pi in C SIM Interaction on behalf of the honest party Sample and send a random r’ {View Ideal i } Pi in C  r : r is random r’ : r’ random

10. Randomized Function and Definition 2 The Real WorldThe Ideal World >> Is this protocol secure? The proof says the protocol is insecure! f(, ) = (r, ) r is a random bit r... Sample r randomly.. Sample r randomly and output r No! {View Real i } Pi in C SIM {View Ideal i } Pi in C  [ {View Real i } Pi in C, {Output Real i } Pi in H ] [ {View Ideal i } Pi in C, {Output Ideal i } Pi in H ]  [{r’, r} | r,r’ random and independent] [{r, r} | r random] Interaction on behalf of the honest party Sample and send a random r’

11. Definition 1 is Enough! { {View Real i } Pi in C } {x1,.,xn, k} { {View Ideal i } Pi in C } {x1,.,xn, k}  { {View Real i } Pi in C,{Output Real i } Pi in H } {x1,..xn, k} { {View Ideal i } Pi in C,{Output Ideal i } Pi in H } {x1,.,xn, k}  >> For deterministic Functions: > View of the adversary and output are NOT co-related. > We can separately consider the distributions > Output of the honest parties are fixed for inputs >> For randomized functions: > We can view it as a deterministic function where the parties input randomness (apart from usual inputs). Compute f((x 1,r 1 ), (x 2,r 2 )) to compute g(x 1,x 2 ;r) where r 1 +r 2 can act as r.

12. Making Indistinguishability Precise Notations: o Security parameter k (natural number) o We wish security to hold for all inputs of all lengths, as long as k is large enough Definition (Function  is negligible): If for every polynomial p(  ) there exists an N such that for all k > N we have  (k) < 1/p(k) Definition (Probability ensemble X={X(a,k)}): o Infinite series, indexed by a string a and natural k o Each X(a,k) is a random variable In our context: o X(x 1,.,x n, k) = { {View Real i } Pi in C } {x1,.,xn, k} (Probability space: randomness of parties) o Y(x 1,.,x n, k) = { {View Ideal i } Pi in C } {x1,.,xn, k} (Probability space: randomness of the corrupt parties and simulator)

13. Computational Indistinguishability o X(x 1,.,x n, k) = { {View Real i } Pi in C } {x1,.,xn, k} (Probability space: randomness of parties) o Y(x 1,.,x n, k) = { {View Ideal i } Pi in C } {x1,.,xn, k} (Probability space: randomness of the corrupt parties and simulator) Definition (Computational indistinguishability of X = {X(a,k)}  c Y = {Y(a,k)}) For every polynomial-time distinguisher* D there exists a negligible function  such that for every a and all large enough k’s: |Pr[D(X(a,k) = 1 ] - Pr[D(Y(a,k) = 1 ]| <  (k) For our case a: (x 1,.,x n ) Alternative def. Adv D (X,Y) = The prob of D guessing the correct distribution |Adv D (X,Y)| < ½ +  (k) distinguisher D = Real Adv A

14. Statistical Indistinguishability o X(x 1,.,x n, k) = { {View Real i } Pi in C } {x1,.,xn, k} (Probability space: randomness of parties) o Y(x 1,.,x n, k) = { {View Ideal i } Pi in C } {x1,.,xn, k} (Probability space: randomness of the corrupt parties and simulator) Definition (Statistical indistinguishability of X = {X(a,k)}  s Y = {Y(a,k)}) For every* distinguisher D there exists a negligible function  such that for every a and all large enough k’s: |Pr[D(X(a,k) = 1 ] - Pr[D(Y(a,k) = 1 ]| <  (k) For our case a: (x 1,.,x n ) Alternative def. Adv D (X,Y) = The prob of D guessing the correct distribution |Adv D (X,Y)| < ½ +  (k) May have unbounded power

15. Perfect Indistinguishability o X(x 1,.,x n, k) = { {View Real i } Pi in C } {x1,.,xn, k} (Probability space: randomness of parties) o Y(x 1,.,x n, k) = { {View Ideal i } Pi in C } {x1,.,xn, k} (Probability space: randomness of the corrupt parties and simulator) Definition (Perfect indistinguishability of X = {X(a,k)}  P Y = {Y(a,k)}) For every* distinguisher D such that for every a and for all k: |Pr[D(X(a,k) = 1 ] - Pr[D(Y(a,k) = 1 ]| = 0 For our case a: (x 1,.,x n ) Alternative def. Adv D (X,Y) = The prob of D guessing the correct distribution |Adv D (X,Y)| = ½ May have unbounded power

16. Definition Applies for Dimension 2 (Networks) Complete Synchronous Dimension 3 (Distrust) Centralized Dimension 4 (Adversary) Threshold/non- threshold Polynomially Bounded and unbounded powerful Semi-honest Static

17. What is so great about the definition paradigm? >> One definition for all > Sum: (x 1 + x 2 + … + x n ) = f(x 1, x 2, …, x n ) > OT: (-, x b ) = f((x 1, x 2 ), b) > BA: (y, y, …,y) = f(x 1, x 2, …, x n ): y = majority(x 1, x 2, …, x n )/default value >> Easy to tweak the ideal world and weaken/strengthen security Real world protocol achieves whatever ideal world achieves >> Coming up with the right ideal world is tricky and requires skill > Will have fun with it in malicious world!

18. Information Theoretic MPC with Semi-honest Adversary and honest majority [BGW88] Dimension 2 (Networks) Complete Synchronous Dimension 3 (Distrust) Centralized Dimension 4 (Adversary) Threshold (t) Unbounded powerful Semi-honest Static Dimension 1 (Models of Computation) Arithmetic Michael Ben-Or, Shafi Goldwasser, Avi Wigderson:Shafi Goldwasser, Avi Wigderson: Completeness Theorems for Non-Cryptographic Fault-Tolerant Distributed Computation (Extended Abstract). STOC 1988.STOC 1988.

19. (n, t) - Secret Sharing Scheme [Shamir 1979, Blackley 1979] Secret s Dealer

20. (n, t) - Secret Sharing Scheme [Shamir 1979, Blackley 1979] Secret s Dealer v1v1 v2v2 v3v3 vn vn Sharing Phase …

21. (n, t) - Secret Sharing Scheme [Shamir 1979, Blackley 1979] Secret s Dealer v1v1 v2v2 v3v3 vn vn Sharing Phase … Less than t +1 parties have no info’ about the secret Reconstruction Phase

22. (n, t) - Secret Sharing Scheme [Shamir 1979, Blackley 1979] Secret s Dealer v1v1 v2v2 v3v3 vn vn Sharing Phase …  t +1 parties can reconstruct the secret Secret s Reconstruction Phase

23. Shamir-sharing: (n,t) - Secret Sharing for Semi-honest Adversaries Secret x is Shamir-Shared if x2x2 x3x3 x n x1x1 … Random polynomial of degree t over F p s.t p>n P1P1 P2P2 PnPn P3P3

24. Reconstruction of Shamir-sharing: (n,t) - Secret Sharing for Semi-honest Adversaries x2x2 x3x3 x n x1x1 P1P1 P2P2 PnPn P3P3 PiPi The same is done for all P i Lagrange’s Interpolation

25. Shamir-sharing Properties Property 2: Any t parties have ‘no’ information about the secret. Pr[secret =s before secret sharing] – Pr[secret =s after secret sharing] = 0 Property 1: Any (t+1) parties have ‘complete’ information about the secret. >> Both proof can be derived from Lagrange’s Interpolation.

26. Lagrange’s Interpolation >> Assume that h(x) is a polynomial of degree at most t and C is a subset of F p of size t+1 >> Poly of degree t >> At i, it evaluates to 1 >> At any other point, it gives 0. Theorem: h(x) can be written as >> Assume for simplicity C = {1,……,t+1} where Proof: Consider LHS – RHS: Both LHS and RHS evaluates to h(i) for every i in C Both LHS and RHS has degree at most t LHS - RHS evaluates to 0 for every i in C and has degree at most t More zeros (roots) than degreeZero polynomial! LHS = RHS

27. Lagrange’s Interpolation >> Poly of degree t >> At i, it evaluates to 1 >> At any other point, it gives 0. Theorem: h(x) can be written as C = {1,……,t+1} where >> are public polynomials >> are public values, denote by r i >> Can be written as the linear combination of h(i)s The combiners are (recombination vector): r 1,….r t+1 Property 1: Any (t+1) parties have ‘complete’ information about the secret.

28. Lagrange’s Interpolation Property 2: Any t parties have ‘no’ information about the secret. Pr[secret =s before secret sharing] – Pr[secret =s after secret sharing] = 0 Proof: For any secret s from F p if we sample f(x) of degree at most t randomly s.t. f(0) = s and consider the following distribution for any C that is subset of F p \ {0} and of size t : ( {f(i)} i in C )uniform distribution in F p t For a fixed s, If not then two different sets of t+1 values will define the same polynomial t coefficients from F p t A unique element from the above distribution

29. Lagrange’s Interpolation Property 2: Any t parties have ‘no’ information about the secret. Pr[secret =s before secret sharing] – Pr[secret =s after secret sharing] = 0 Proof: For any secret s from F p if we sample f(x) of degree at most t randomly s.t. f(0) = s and consider the following distribution for any C that is subset of F p \ {0} and of size t : ( {f(i)} i in C )uniform distribution in F p t For a fixed s, t coefficients from F p t A unique element from the above distribution f s : F p t FptFpt Uniform distribution bijective Uniform distribution For every s, uniform dist and independent of dist. of s

30. Lagrange’s Interpolation Property 2: Any t parties have ‘no’ information about the secret. Pr[secret =s before secret sharing] – Pr[secret =s after secret sharing] = 0 Proof:

32. (n,t) Secret Sharing s : (n,t) Secret Sharing of secret s For MPC: Linear (n,t) Secret Sharing s 1 s 2 c s s1s1 s2s2 c : public constant s   from Linearity: The parties can do the following Linear

33. Linearity of (n, t) Shamir Secret Sharing a 1 a 2 a 3 a 11 22 33 a 1 a 2 a 3 a b 1 b 2 b 3 b b b 1 b 2 b 3 +++ each party does locally c 1 c 2 c 3

34. Linearity of (n, t) Shamir Secret Sharing a 1 a 2 a 3 a 11 22 33 a 1 a 2 a 3 a b 1 b 2 b 3 b b b 1 b 2 b 3 ++ + c 1 c 2 c 3 a  b c 1 c 2 c 3 a+b Addition is Absolutely free

35. Linearity of (n, t) Shamir Secret Sharing 11 22 33 a 1 a 2 a 3 a 1 a 2 a 3 a a cc cc cc c is a publicly known constant d 1 d 2 d 3 c  a d 1 d 2 d 3 c  a Multiplication by public constants is Absolutely free

36. Non-linearity of (n, t) Shamir Secret Sharing a 1 a 2 a 3 b 1 b 2 b 3 b a 11 22 33 a 1 a 2 a 3 a b b 1 b 2 b 3    d 1 d 2 d 3 a  b d 1 d 3 d 2 a  b Multiplication of shared secrets is not free

37. Secure Circuit Evaluation x1x1 x2x2 x3x3 x4x4     c y

38. 2 1 5 9     y 3

39. 1.(n, t)- secret share each input     2159 3

40. Secure Circuit Evaluation     2159 2. Find (n, t)-sharing of each intermediate value 1.(n, t)- secret share each input 3

41. Secure Circuit Evaluation     3 215934814445 2. Find (n, t)-sharing of each intermediate value 1.(n, t)- secret share each input

42. Secure Circuit Evaluation     2159348 Linear gates: Linearity of Shamir Sharing - Non-Interactive 14445 3 2. Find (n, t)-sharing of each intermediate value 1.(n, t)- secret share each input

43. Secure Circuit Evaluation     2159348 Non-linear gate: Require degree- reduction Technique. Interactive 45144 3 2. Find (n, t)-sharing of each intermediate value 1.(n, t)- secret share each input Linear gates: Linearity of Shamir Sharing - Non-Interactive

44. Secure Circuit Evaluation     2159 1.No inputs of the honest parties are leaked. 2. No intermediate value is leaked. 34845 144 Privacy follows (intuitively) because: 3