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Conditional Computational EntropyPowerPoint Presentation

Conditional Computational Entropy

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ConditionalComputational Entropy

Chun-Yuan Hsiao (Boston University, USA)

Joint work with

Chi-Jen Lu (Academia Sinica, Taiwan)

Leonid Reyzin (Boston University, USA)

Does Pseudo-Entropy = Incompressibility?

How to extract more pseudorandom bits?

Shannon Entropy

H(X)Exx [log( Pr[X x] )]

X

2.58 bits

Usually in crypto: minimum instead of average(a.k.a. min-entropy H(X) )

means

indistinguishable

(in polynomial time)

PRG

(Blum-Micali-Yao)

Pseudo-EntropyX has pseudo-entropykifY, H(Y) = kandX Y

HHILL(X) = k [Håstad,Impagliazzo,Levin,Luby]

X

Computational Entropy

(version 1: HILL)

Entropy vs Compressibility

Shannon's Theorem

| X | = 60

H(X) = 40

H(X)

X

C(X)

D(C(X))

= X

Compression length

C(X)

Compress

(C)

Decompress

( D)

Compression-Entropy

Computational Entropy

- X has computational entropy k, if we cannot efficiently compress X shorter than k
HYao(X) = k [Yao82]

- [Barak,Shaltiel,Wigderson03] gave min-entropy formulation

(version 2: Yao)

any subset of the support of X cannot be compressed

Computational Entropy

- Version 1: HILL
HHILL(X) = k, ifY, H(Y) = kandX Y

- Version 2: Yao
HYao(X) = k, if we cannot efficiently compress X shorter than k

Question [Impagliazzo99]:

Are these equivalent definitions?

?

?

(Pseudo-)Entropy vs Compressibility

Is computational analogue true?

Recall Shannon’s Theorem:

?

pseudo-

entropy compression length

efficient

Computational Entropy

- Version 1: HILL
HHILL(X) = k, ifY, H(Y) = kandX Y

- Version 2: Yao
HYao(X) = k, if we cannot efficiently compress X shorter than k

?

Cryptographic Motivation

pseudo

H(X)

randombits

computational

Extractor

(Hashing)

entropy

key

Which computational entropy?all extractors work for HHILL(X);some work for HYao(X) [BSW03]

e.g. gab

If HYao(X) > HHILL(X)

may get longer a key

(by using the right extractor)

How?

Our results1. distribution* X such that HYao(X) > HHILL(X)

2. bits extracted via HYao> bits extracted via HHILL

3. Define computational entropy, version 3: new, unpredictability-based definition

0. New† notion: conditional computational entropy†previously used, but never formalized

*conditional distribution

Our Definition: ConditionalComputational Entropy

- HILL:
HHILL(X | Z)= k

if Y,H(Y | Z)= kand (X , Z)(Y , Z)

Z

X

Y

?

Our Definition: ConditionalComputational Entropy

- Yao:
HYao(X | Z)= k

if we cannot efficiently compress X shorter than k

Z

Z

D(C(X , Z) ,Z)

=X

C( X , Z)

Conditional is Everywhere in Crypto

- In cryptography, adversaries usually have additional information
- entropic secret: gab | adversary is givenga, gb
- entropic secret: x | adversary is givenf(x)
- entropic secret: SignSK(m)| adversary is givenPK

- To make extraction precise, must talk about conditional entropy
- Conditional computational entropy has been used implicitly in [Gennaro,Krawczyk,Rabin04],but never defined explicitly for HILL and Yao

Our results

0. New† notion: conditional computational entropy†previously used, but never formalized

1. pair (X, Z) such that HYao(X | Z) >> HHILL(X | Z)

(where Z is a uniform string)

2. Extract more pseudorandom bits from (X , Z) by considering its Yao-entropy

3. Define computational entropy, version 3: Hunp(X | Z) = k, if efficient M, Pr[ M(Z) = X ] < 2k

- Allows to talk about entropy of singletons, like x | f(x)
- Can’t be defined unconditionally

Yao Entropy > HILL Entropy [Wee03]

(oracle separation)

[this paper]

Length

increasing

random

function

f

PRG

G

{0,1}n

{0,1}3n

X

Caveat: need uniZK [Lepinski,Micali,Shelat05]

X = ( G( Un ) , )

Z = NIZKreference string

Non-

Interactive

Zero-

Knowledge

Membership

oracle

m

Yes

No

Summary

- Conditional Version 1: HHILL (X | Z)
- Conditional Version 2: HYao (X | Z)
- Conditional Version 3: Hunp (X | Z)

Computational Entropy:

Can extract more from Yao than HILL(even unconditionally)

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