1. Computational Entropy Joint works with Iftach Haitner (Tel Aviv), Thomas Holenstein (ETH Zurich), Omer Reingold (MSR-SVC), Hoeteck Wee (George Washington U.), and Colin Jia Zheng (Harvard) TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAA A Salil Vadhan Harvard University (on sabbatical at Microsoft Research SVC and Stanford)

2. Complexity-Based Cryptography  Shannon `49: Information-theoretic security is infeasible. –|Key| ¸ |All Encrypted Data| –On a standard, insecure communication channel.  Diffie & Hellman `76: Complexity-based cryptography –Assume adversary has limited computational resources –Base cryptography on hard computational problems –Enables public-key crypto, digital signatures, …

3. One-Way Functions [DH76]  Candidate: f(x,y) = x ¢ y Formally, a OWF is f : {0,1} n ! {0,1} n s.t.  f poly-time computable  8 poly-time A Pr[A(f(X)) 2 f -1 (f(X))] = 1/n ! (1) for X Ã {0,1} n x f(x) easy hard

4. OWFs & Cryptography one-way functions pseudorandom generators target-collision-resistant hash functions (UOWHFs) statistically hiding commitments pseudorandom functions statistically binding commitments zero-knowledge proofs statistical ZK arguments private-key encryption MACs digital signatures secure protocols & applications [HILL90] [R90] [HNORV07] [GGM86] [N89] [GMW86] [NY89][BCC86]

5. OWFs & Cryptography one-way functions pseudorandom generators target-collision-resistant hash functions (UOWHFs) statistically hiding commitments pseudorandom functions statistically binding commitments zero-knowledge proofs statistical ZK arguments private-key encryption MACs digital signatures secure protocols & applications [HILL90] [R90] [HNORV07] [GGM86] [N89] [GMW86] [NY89][BCC86]

6. Computational Entropy [Y82,HILL90,BSW03] Question: How can we use the “raw hardness” of a OWF to build useful crypto primitives? Answer (today’s talk):  Every crypto primitive amounts to some form of “computational entropy”.  One-way functions already have a little bit of “computational entropy”.

7. Entropy Def: The Shannon entropy of r.v. X is H(X) = E x à X [log(1/Pr[X=x)]  H(X) = “Bits of randomness in X (on avg)”  0 · H(X) · log |Supp(X)|  Conditional Entropy: H(X|Y) = E y à Y [H(X| Y=y )] X concentrated on single point X uniform on Supp(X)

8. Worst-Case Entropy Measures  Min-Entropy: H 1 (X) = min x log(1/Pr[X=x])  Max-Entropy: H 0 (X) = log |Supp(X)| H 1 (X) · H(X) · H 0 (X)

9. Computational Entropy A poly-time algorithm may “perceive” the entropy of X to be very different from H(X). Example: X=output of a “pseudorandom generator”

10. Computational Entropy A poly-time algorithm may “perceive” the entropy of X to be very different from H(X).  Example: a pseudorandom generator [BM82,Y82] G: {0,1} m ! {0,1} n –G(U m ) “computationally indistinguishable” from U n –But H(G(U m )) · m. e.g. G(N,x) = (lsb(x),lsb(x 2 mod N), lsb(x 4 mod N),…) for N=pq, x 2 Z N * is a PRG if factoring is hard [BBS82,ACGS82]. Def [GM82] : X ´ c Y iff 8 poly-time T Pr[T(X)=1] ¼ Pr[T(Y)=1]

11. Pseudoentropy Def [HILL90]: X has pseudoentropy ¸ k iff there exists a random variable Y s.t. 1.Y ´ c X 2.H(Y) ¸ k Interesting when k > H(X), i.e. Pseudoentropy > Real Entropy

12. OWFs & Cryptography one-way functions pseudorandom generators target-collision-resistant hash functions (UOWHFs) statistically hiding commitments pseudorandom functions statistically binding commitments zero-knowledge proofs statistical ZK arguments private-key encryption MACs digital signatures secure protocols & applications [HILL90] [R90] [HNORV07] [GGM86] [N89] [GMW86] [NY89][BCC86] pseudoentropy

13. Application of Pseudoentropy Thm [HILL90]: 9 OWF ) 9 PRG Proof idea: OWF X with pseudo-min-entropy ¸ H 0 (X)+poly(n) X with pseudoentropy ¸ H(X)+1/poly(n) PRG to discuss repetitions hashing

14. Computational Setting: For 1-1 OWF f, X Ã {0,1} n : H(X|f(X))=0, but Information Theory: For jointly distributed (X,Y): ? Pseudoentropy from OWF: Intuition 8 poly-time A Pr[A(f(X))=X] · 1/n ! (1) X has “unpredictability entropy” ¸ ! (log n) given f(X) X has pseudoentropy ! (log n) given f(X) 8 function A Pr[A(Y)=X] · p X has “average min-entropy” ¸ log(1/p) given Y H(X|Y) ¸ log(1/p) def [HLR07] def [DORS04]

15. Computational Setting: For 1-1 OWF f, X Ã {0,1} n : H(X|f(X))=0, but FALSE! T(x,y) = [[f(x)= ? y]] distinguishes (X,f(X)) from every (Z,f(X)) with H(Z|f(X))>0 Challenges:  How to convert unpredictability into pseudoentropy?  When f not 1-1, unpredictability can be trivial. Pseudoentropy from OWF: Intuition 8 poly-time A Pr[A(f(X))=X] · 1/n ! (1) X has “unpredictability entropy” ¸ ! (log n) given f(X) X has pseudoentropy ! (log n) given f(X) def [HLR07]

16. Pseudoentropy from OWF  Thm [HILL90]: W=(f(X),H,H(X) 1,…H(X) J ) has pseudoentropy ¸ H(W)+ ! (log n)/n –H : {0,1} n ! {0,1} n a certain kind of hash func. –X Ã {0,1} n, J Ã {1,…,n}.  Thm [HRV10,VZ11]: (f(X),X 1,…,X n ) has “next-bit pseudoentropy” ¸ n+ ! (log n). –No hashing! –Total amount of pseudoentropy known & > n. –Get full ! (log n) bits of pseudoentropy.

17. Next-bit Pseudoentropy  Thm [HRV10,VZ11]: (f(X),X 1,…,X n ) has “next-bit pseudoentropy” ¸ n+ ! (log n).  Note: (f(X),X) easily distinguishable from every random variable of entropy > n.  Next-bit pseudoentropy: 9 (Y 1,…,Y n ) s.t. –(f(X),X 1,…,X i ) ´ c (f(X),X 1,…,X i-1,Y i ) – H(f(X))+  i H(Y i |f(X),X 1,…,X i-1 ) = n+ ! (log n).

18. Consequences  Simpler and more efficient construction of pseudorandom generators from one-way functions.  [HILL90,H06]: OWF f of input length n ) PRG G of seed length O(n 8 ).  [HRV10,VZ11]: OWF f of input length n ) PRG G of seed length O ~ (n 3 ).

19. Pseudoentropy, Unpredictability wrt KL Divergence  Thm [VZ11]: Let (Y,Z) 2 {0,1} n £ {0,1} O(log n). The pseudoentropy of Z given Y is ¸ H(Z|Y)+ ± m There is no probabilistic poly-time A s.t. D((Y,Z)||(Y,A(Y)) · ±. [D = Kullback-Liebler Divergence]  Special case: H(Z|Y)=0 –“Z has pseudoentropy ¸ ± ” iff “hard to predict Z with divergence · ± ” –Can’t take Z=f -1 (Y) for 1-1 OWF f since |f -1 (Y)|=n.

20. Pseudoentropy, Unpredictability wrt KL Divergence  Thm [VZ11]: Let (Y,Z) 2 {0,1} n £ {0,1} O(log n). The pseudoentropy of Z given Y is ¸ H(Z|Y)+ ± m There is no probabilistic poly-time A s.t. D((Y,Z)||(Y,A(Y)) · ±. [D = Kullback-Liebler Divergence]  Analogue of Impagliazzo’s Hardcore Thm [I95,N95,H05,BHK09] for Shannon entropy rather than min-entropy.

21. Next-Bit Pseudoentropy from OWF: Proof Sketch f a one-way function Given f(X), hard to achieve divergence O(log n) from X (f(X),X 1,…,X n ) has next-bit pseudoentropy ¸ n+ ! (log n) Given (f(X),X 1,…,X J ), hard to achieve divergence O(log n)/n from X J+1 thm Given (f(X),X 1,…,X J ), X J+1 has pseudoentropy ¸ entropy+ ! (log n)/n

22. OWFs & Cryptography one-way functions pseudorandom generators target-collision-resistant hash functions (UOWHFs) statistically hiding commitments pseudorandom functions statistically binding commitments zero-knowledge proofs statistical ZK arguments private-key encryption MACs digital signatures secure protocols & applications [HRV10, VZ11] [R90] [HNORV07] [GGM86] [N89] [GMW86] [NY89][BCC86] next-bit pseudoentropy

23. Application of Next-Bit Pseudoentropy Thm [HILL90]: 9 OWF ) 9 PRG Proof outline [HRV10]: OWF Z’ with next-block pseudo-min-entropy ¸ |seed|+poly(n) Z=(f(U n ),U n ) with next-bit pseudoentropy ¸ n+ ! (log n) PRG done repetitions hashing

24. OWFs & Cryptography one-way functions pseudorandom generators target-collision-resistant hash functions (UOWHFs) statistically hiding commitments pseudorandom functions statistically binding commitments zero-knowledge proofs statistical ZK arguments private-key encryption MACs digital signatures secure protocols & applications [HRV10, VZ11] [R90] [HNORV07] [GGM86] [N89] [GMW86] [NY89][BCC86] next-bit pseudoentropy

25. OWFs & Cryptography one-way functions pseudorandom generators target-collision-resistant hash functions (UOWHFs) statistically hiding commitments pseudorandom functions statistically binding commitments zero-knowledge proofs statistical ZK arguments private-key encryption MACs digital signatures secure protocols & applications [HRV10, VZ11] [GGM86] [N89] [GMW86] [NY89][BCC86] next-bit pseudoentropy inaccessible entropy [HRVW09, HHRVW10]

26. Inaccessible Entropy [HRVW09,HHRVW10]  Example: if h : {0,1} n ! {0,1} n-k is collision- resistant and X Ã {0,1} n, then –H(X|h(X)) ¸ k, but –To an efficient algorithm, once it produces h(X), X is determined ) “accessible entropy” 0. –Accessible entropy ¿ Real Entropy!  Thm [HRVW09]: f a OWF ) (f(X) 1,…,f(X) n,X) has accessible entropy n- ! (log n). –Cf. (f(X),X 1,…,X n ) has pseudoentropy n+ ! (log n).

27. Conclusion Complexity-based cryptography is possible because of gaps between real & computational entropy. “Secrecy” pseudoentropy > real entropy “Unforgeability” accessible entropy < real entropy

28. Research Directions  Formally unify inaccessible entropy and pseudoentropy.  OWF f : {0,1} n ! {0,1} n ) Pseudorandom generators of seed length O(n)?  More applications of inaccessible entropy in crypto or complexity (or mathematics?)