1 concurrency and zero-knowledge protocols amit sahai mit laboratory for computer science

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ConcurrencyConcurrencyandand

Zero-Knowledge ProtocolsZero-Knowledge Protocols

Amit Sahai

MIT Laboratory for Computer Science

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Zero-knowledge Proofs Zero-knowledge Proofs [GMR85][GMR85]

• One party (“the prover”) convinces another party (“the verifier”) that some assertion is true,

• The verifier learns nothing except that assertion is true!

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Zero-knowledge Proofs (cont.)Zero-knowledge Proofs (cont.)

Vast applicability throughout Cryptography:

• Identification / Authentication Protocols: [GMR, FS, …] Prove knowledge without revealing it.

• “Next Generation” Protocols: [GMW]

• Key Escrow [M, MS, VvT, …]

• Electronic Elections [C, CF, C, OO, … ]

• Anonymous Credentials [C, CvH, LRSW, …]

• Dealerless Poker [GMW, BCR, C, …]

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AssumptionsAssumptions Almost all previous research assumes:

• Sequential communication

• At most 2 parties communicating at any given time

• Mutually aware, cooperating parties

ProverVerifier

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The InternetThe Internet

EbayYahoo

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The InternetThe Internet• Concurrent, interleaved communication

• Mutually unaware parties, acting locally

EbayYahoo

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Challenge: Challenge: Global Coordinated AttackGlobal Coordinated Attack

Yahoo

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Global Coordinated AttackGlobal Coordinated Attack

Yahoo

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Our Context: Zero KnowledgeOur Context: Zero Knowledge

Prover

HonestVerifier

Corrupted Verifiers tryingto extract Prover’s secrets.

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The GoalThe Goal

Extend theory of Zero-Knowledge Protocolsto provide security in Internet setting.

• [Dwork, Naor, Sahai -- STOC ‘98]

• [Dwork, Sahai -- Crypto ‘98]

+ Ongoing work

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OutlineOutline

1. Zero Knowledge:

Definitions and example

2. What goes wrong

3. How to fix it

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Interactive Proof SystemInteractive Proof Systemv1

p1

v2

pk

accept/reject

Prover Verifier

Interactive protocol where Prover tries to convince probabilistic Verifier that assertion x is true.

• When x is true, Verifier always accepts.

• When x is false, Verifier accepts only with negligible prob. no matter what strategy Prover uses.

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(Ordinary) Zero-Knowledge(Ordinary) Zero-Knowledge [GMR][GMR]

v1

p1

v2

pk

accept/reject

When assertion is true, can simulate interaction with any Verifier, w/o access to Prover.

Formally, for every verifier, there is probabilistic efficient simulator such that, when given a true assertion, simulator output is computationally indistinguishable from Verifier’s actual view of interaction with Prover.

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Zero Knowledge Zero Knowledge [GMR][GMR]

?

v1

p1

v2

pk

accept/reject

v1

p1

v2

pk

accept/reject

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When assertion is true, can simulate interaction with any Adversary, w/o access to Prover.

V1 V2 … Vn

1 2 1 2 .. 1 2 3 4 .. 3 4 3 4

Concurrent Zero KnowledgeConcurrent Zero Knowledge

Formally, for every Adversary, there is a probabilistic efficient simulator such that, when given a true assertion, simulator output is computationally indistinguishable from Adversary’s actual view of interaction with Prover.

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Deniable Message Deniable Message AuthenticationAuthentication

Monica

Linda

Bill

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Example:Example:Zero-Knowledge ProofsZero-Knowledge Proofs

for NPfor NP

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CryptographicCryptographicCommitmentCommitment

• Public Key Encryption Scheme (PK,SK)

• Assume EPK is always one-to-one.

• To commit to a string x, I send y = EPK(x;r).

• To open the commitment, I reveal (x,r).

• Commitment is secret.

• Because EPK is 1:1, can’t change my mind about x.

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The Power of NPThe Power of NP• NP is very useful cryptographically, e.g.:

• Say y=EPK(x;r) and y’=EPK’(x’;r’).

• “y and y’ are encryptions of same message” is in NP!

• Say f is efficiently computable.

• “y’ is the encryption of f applied to the decryption of y” NP

• If we could prove NP statements in ZK, ...

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NP-CompletenessNP-Completeness• Amazing thing about NP:

• There are languages complete for NP!

• e.g. Graph 3-Colorability

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NP-Completeness (cont.)NP-Completeness (cont.)

• y=EPK(x;r) and y’=EPK’(x;r’) “y and y’ are encryptions of same message”

reduction

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ZK Proof for ZK Proof for Graph 3-ColorabilityGraph 3-Colorability

• Input: Graph G=(V=1, …, n,E).

• Prover Knows: 3-coloring c: V R,B,G

• First, Prover picks random permutation

: R,B,G R,B,G, and applies to c:

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ZK Proof (cont.)ZK Proof (cont.)

Prover Verifier

Commit((c(1)), …, Commit((c(n))

e(i,j) R E

Reveal (c(i) and (c(j)

Verifier accepts if (c(i) (c(j)

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Related WorkRelated Work

• Large body of work on Concurrent Security:

• Focus: Integrity/Consistency of System State

• Locking and preventing Deadlock, Starvation

• Preventing inconsistent data reads

• Synchronizing databases

• Our Focus: Completely Different

• Not state, but preventing information leakage from protocol abuse.

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Problem: Coordinated AttacksProblem: Coordinated Attacks

Yahoo

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Yahoo

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EbayYahoo

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Yahoo

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The InternetThe Internet

EbayYahoo

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Modern Cryptography:Modern Cryptography:

Zero-Knowledge

Proofs

Amit SahaiMIT

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CryptographyCryptography

• Encryption, Digital Signatures, etc.

• Protocols!

• Identification, Authentication...

• Electronic Elections

• Pseudonym Systems

• ...

• Today’s focus: Zero-Knowledge Proofs!

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ProofsProofs

• What is a proof?

Lemma 1: blah blah blah.

Proof: blah blah blah �

Lemma 2: blah blah.

Proof: blah blah blah

blah blah blah �

blah blah blah! QED.

© Microsoft Proof WizardTM.

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ProofsProofs

• What is a proof to a computer?

• Verify(assertion, Proof) = accept

• Verify(assertion, Proof) = reject

• What kinds of assertions+proofs can computers verify?

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Example: SatisfiabilityExample: Satisfiability• Consider assertions of form:

• “Formula is satisfiable”

• e.g. = (x1 x2) (x2 x3)

• Proof = Satisfying Assingment

• x1 = true, x2 = false, x3 = true

• Verify(, (a1,a2,a3)):

• Plug in a1,a2,a3 into .

•Accept if becomes true.

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NPNP

• NP = assertion “types” (aka languages) with proofs that are:

• efficiently computer-verifiable

• reasonable length

• Very rich class.

• e.g. Satisfiability NP

• Not the end of the story!

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ProbabilisticProbabilistic Proofs Proofs

• Must proof be totally convincing?

• Alternative:

• If proof correct, Verifier accepts always

• If proof wrong, Verfier rejects with high prob.

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InteractiveInteractive Proofs Proofs

• Prover and Verifier talk back and forth.

• Prover tries to convince Verifier that assertion is true.

• If assertion is false, Prover fails with high prob.

• Now, Proof is a Protocol.

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p1

v2

pk

accept/reject

Prover Verifier

Interactive protocol where Prover tries to convince probabilistic Verifier that x is true.

• When x is true, Verifier accepts always.

• When x is false, Verifier rejects w.p. (1-2-

100) no matter what strategy Prover uses.

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p1

v2

pk

accept/reject

Prover Verifier



• When x is false, Verifier rejects w.p. (1-2-

100) no matter what strategy Prover uses.

1/2

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Zero KnowledgeZero Knowledge

• Prover convinces Verifier, but...

• Verifier learns nothing except that assertion is true!

• What does that mean??

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Defining Zero KnowledgeDefining Zero Knowledge

• Natural Suggestion:

• Verifier should not be able to prove assertion to anyone else.

• …what if Verifier already knew how to prove assertion?

• ...maybe Verifier learned something else...

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Magic TricksMagic Tricks

• Magic tricks are like zero-knowledge proofs:

• Good magic tricks reveal nothing about how they work.

• What makes a magic trick good?

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A Magic TrickA Magic Trick• Two balls: Purple and Red, otherwise identical

• Blindfolded Magician

• You give a random ball to magician

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A Magic Trick (cont.)A Magic Trick (cont.)• Magician tells you the color!

• Magician proves he can distinguish balls blindfolded.

• You learn nothing except this.

Abracadabra,Goobedy goo!

It is Red!

Wow! He’sso cool!

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A Magic Trick (cont.)A Magic Trick (cont.)• You knew exactly what magician was going to do.

• And he did it!

• Since you knew to begin with, you could not have learned anything new!

It’s Red!

I knew hewould say that.

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Zero KnowledgeZero Knowledge• Idea for definition:

• Verifier “knows” what is going to happen.

• CS-speak: Verifier can simulate it herself!

Abracadabra,Goobedy goo!

It is Red!

Simulation

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Zero-Knowledge ProofZero-Knowledge Proof

v1

p1

v2

pk

accept/reject

When assertion is true, Verifier can simulate her view of the interaction on her own.

Formally, there is probabilistic poly-time simulator such that, when given a true assertion, simulator output is computationally indistinguishable from Verifier’s actual view of interaction with Prover.

Note: ZK for honest verifier only.

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Zero Knowledge ProofZero Knowledge Proof

?

v1

p1

v2

pk

accept/reject

v1

p1

v2

pk

accept/reject

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Dishonest VerifiersDishonest Verifiers

Ha ha!

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Zero-Knowledge ProofZero-Knowledge Proof

v1

p1

v2

pk

accept/reject

When assertion is true, any Verifier can simulate her view of the interaction on her own.

Formally, for every verifier, there is probabilistic poly-time simulator such that, when given a true assertion, simulator output is computationally indistinguishable from Verifier’s actual view of interaction with Prover.

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Zero-Knowledge ProofsZero-Knowledge Proofsfor NPfor NP

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Another Magic TrickAnother Magic Trick• Magician asks you to think of either

• “Apple” or

• “Banana”

• Magician then gives you a sealed box.

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Mind ReadingMind Reading• You tell Magician what you were thinking.

I was thinkingof a banana.

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Banana

Mind Reading (cont.)Mind Reading (cont.)• Magician tells you to open box, and read piece of paper in box.

• Magician proves he can predict what you will say.

How did hedo that!!

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Mind Reading (cont.)Mind Reading (cont.)• Again, you knew what was going to happen. Zero-Knowledge

I was thinkingof a banana.

Simulation

Banana

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Mind Reading (cont.)Mind Reading (cont.)• But why was it convincing?

• Because Magician committed to his guess before you told him.

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CryptographicCryptographicCommitmentCommitment

• Public Key Encryption Scheme (PK,SK)

• Assume EPK is always one-to-one.

• To commit to a string x, I send y = EPK(x;r).

• To open the commitment, I reveal (x,r).

• Commitment is secret.

• Because EPK is 1:1, can’t change my mind about x.

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The Power of NPThe Power of NP• NP is very useful cryptographically, e.g.:

• Say y=EPK(x;r) and y’=EPK’(x’;r’).

• “y and y’ are encryptions of same message” is in NP!

• Say f is efficiently computable.

• “y’ is the encryption of f applied to the decryption of y” NP

• If we could prove NP statements in ZK, ...

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NP-CompletenessNP-Completeness• Amazing thing about NP:

• There are languages complete for NP!

• e.g. Graph 3-Colorability

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NP-Completeness (cont.)NP-Completeness (cont.)

• y=EPK(x;r) and y’=EPK’(x;r’) “y and y’ are encryptions of same message”

reduction

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ZK Proof for ZK Proof for Graph 3-ColorabilityGraph 3-Colorability

• Input: Graph G=(V=1, …, n,E).

• Prover Knows: 3-coloring c: V R,B,G

• First, Prover picks random permutation

: R,B,G R,B,G, and applies to c:

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Prover Verifier

Commit((c(1)), …, Commit((c(n))

e(i,j) R E

Reveal (c(i) and (c(j)

Verifier accepts if (c(i) (c(j)

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ZK Proof: AnalysisZK Proof: Analysis• Suppose Graph is NOT 3-colorable.

• Then at least one edge where colors equal.

Verifier catches with prob. 1/m.

• Repeat protocol 100m times, Verifier catches with prob. (1-2-100)

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ZK Proof: Analysis (cont.)ZK Proof: Analysis (cont.)• Why Zero-Knowledge? Verifier knows what will happen.

• Simulator:

• Pick e(i,j) R E

• Pick random different colors a,b.

• Commit to arbitrary values for all colors except for i and j.

• For i and j, commit to a,b.

• Imitate rest of protocol.

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SimulatorSimulator

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ZK Proof (Simulated)ZK Proof (Simulated)

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ZK Proof: Analysis (cont.)ZK Proof: Analysis (cont.)• Only difference between real & simulated:

• In real life, commitments are to valid coloring.

• In simulator, commitments are to invalid coloring.

• But commitments are secret, by security of encryption scheme.

Simulator output and real life are computationally indistinguishable.

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ZK Proof: Analysis (cont.)ZK Proof: Analysis (cont.)

• This is proof of ZK for Honest Verifier.

• Same protocol ZK for Dishonest Verifiers.

• Proof: same idea, more technical.

• Not surprising...

• Verifier’s only job: pick random edge.

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ZK Proof in a nutshellZK Proof in a nutshell

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ConsequencesConsequences

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IdentificationIdentification• Most basic application of ZK: Identification.

• To prove identity, just prove in ZK that some graph is 3-colorable.

• 3-coloring is like password

• Even the computer you are logging on to will never find out your password!

Idea used to make signatures too.

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Bigger PictureBigger Picture• Anonymity:

• Anonymous Credentials

• Pseudonyms (www.zeroknowledge.com)

• e-cash

• Fair exchange

• Distributed Encryption, Signatures

• General Multi-Party Computation

• Mental Poker

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Mental PokerMental Poker• Want to play poker totally in your mind?

• No physical cards.

• No trusted dealer.

• Main Problem: How to deal cards fairly?

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Mental Poker (cont.)Mental Poker (cont.)• Basic idea: Each player shuffles deck, by picking random permutation i.

• Player i gets card 1(2(..(n(i))..).

• No player can control his card.

• Might as well pick random i.

• Shuffle is random + hidden.

• But how does player i get proper card?

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Mental Poker (cont.)Mental Poker (cont.)• Player i wants card 1(2(..(n(i))..)

• Player i asks for n(i), n-1(n(i)), ...

• Say Player i needs k(x):

• Use “Oblivious Transfer”:

• Player i finds out k(x) for one value x.

• Player k does not learn x.

• Uses ZK as subroutine.

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Mental Poker (cont.)Mental Poker (cont.)• Problem: Player k may not give correct k(x).

• Solution:

• Every player commits at beginning to k(1),…,k(52)

• Player k proves in ZK that it gave correct value for k(x)

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Mental Poker (cont.)Mental Poker (cont.)• Problem: Player i may not ask for correct x.

• Solution:

• Player i proves in ZK that it is asking for correct x each time.

Each player gets proper random cards at end of “dealing” phase.

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Mental Poker (cont.)Mental Poker (cont.)• At end of game, if Player i reveals card:

• Player n opens commitment to n(i)

• Player n-1 opens commitment to n-

1(n(i))

• ...

• Player 1 opens commitment to 1(2(..(n(i))..)

• All players verify.

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Any Mental GameAny Mental Game• Using these techniques, can actually play any mental game!

• For any efficient function f, n players with secret inputs x1, ..., xn can:

• Learn y=f(x1, ..., xn) s.t.

• No players learn anything except y.

• In particular, x1, ..., xn still secret.

• e.g. Two people can figure out who has bigger salary, without revealing salary!

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ConclusionsConclusions• Zero Knowledge Proofs

• Simple, beautiful idea.

• Fundamental to Cryptography

• Can prove all NP statements in ZK (assuming one-way functions exist)

• Have a great vacation!

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Mental Poker (cont.)Mental Poker (cont.)• Player i needs k(x).

• Use “Oblivious Transfer”:

• Player k commits to k(1),…,k(n) (at start)

• “Player i gets k(x) without Player k finding out x”

• Player i proves in ZK that only got 1 value.

• Player k proves in ZK that value is consistent with commitment.

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ProverVerifier

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ProverVerifier

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ProverVerifier

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ProverVerifier

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Example: GRAPH ISOMORPHISM [GMW86]

10 ,GG Graphs :Input

.0G

H

ofcopy isomorphic random Let

.1,0R

coin Flip

.HGcoin ifAccept

H

1.

2.

4.

Prover Verifier

Claim: Protocol is an (honest ver) SZK proof.

10 GG :YES

10 GG :NO

coin

3.

.HGcoin and between misomorphis

(random) a be Let

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I love you.

Mind ReadingMind Reading• Two balls: Purple and Red, otherwise identical

• Blindfolded Magician

• You give a random ball to magician

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A Magic Trick (cont.)A Magic Trick (cont.)• You knew exactly what magician was going to do.

• He did it!

• Since you knew to begin with, you could not have learned anything new!

It’s Red!

I knew hewould say that.

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A Magic TrickA Magic Trick

•

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p1

v2

pk

accept/reject

Prover Verifier



• When x is false, Verifier rejects w.p. 1/2 no matter what strategy Prover uses.

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PhilosophyPhilosophy

Is my random numbergenerator secure?

System Designerfor Hospital

Will my protocolswork securely

together?Is it secure vs.

attack A?

Is it secure vs.attack B?

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Holy GrailHoly Grail

• Guarantee: Nobody can break system in 100 years.

• Unfortunately, we don’t know how to do prove such theorems.

Need to make assumptions.

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One Approach...One Approach...

It’s so complicated!It must be secure!

Cryptosystem XYZ(Patent Pending)

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One Approach… (cont.)One Approach… (cont.)

Cryptosystem XYZ Broken 2 Days After

Release!

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ExamplesExamples

• PKCS #1 (Encryption Standard).

• DVD Encryption

• Digital Cellular Phone Encryption (GSM)

• …

• Lesson: Intuition often fails to hold for cryptography. Must be cautious!

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AssumptionsAssumptions

• Some assumptions have held up over the years.

• e.g. problems believed to be hard:

• Discrete Logarithm: Given y=gx (mod p), find x.

• RSA: Given y=xe (mod N=pq), find x.

Key: Red = Secret, Blue = Known

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SecuritySecurityCryptographic Primitives

RSA Discrete Log

Assumptions

“My encryption scheme is secure against CPA if RSA is hard to invert.”

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Better SecurityBetter SecurityCryptographic Primitives

RSA Discrete Log

Assumptions

One Way Functions

Lattice Problems ...

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Better SecurityBetter SecurityCryptographic Primitives

RSA Discrete Log

Assumptions

One Way Functions

Lattice Problems...

“My signature scheme is secure against CMA if One-Way Functions exist…”

“One-Way Functions exist if either RSA is hard, or Discrete Log is hard, or …”

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PhilosophyPhilosophy

Cryptography: Systematically address as many concerns as we can.

Will my protocolswork securely

together?Is it secure vs.

attack A?

Is it secure vs.attack B?

Is my random number generator

secure?

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Randomness: Why?Randomness: Why?• Example: Public-Key Encryption

• Deterministic Encryption?

• Two possible messages:

• “Attack!”

• “Retreat!”

Completely insecure!

Need Randomization.

• Many other examples throughout Crypto.

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RandomnessRandomness• True randomness hard to come by.

• Can get a source with moderate entropy.

• Mouse/Keyboard movements

• Radioactive decay

• Refine a few truly random bits.

• Need many more!

Need to generate Pseudo-Random bits from a few truly random bits

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Pseudo-Random GeneratorPseudo-Random Generator(PRG)(PRG)

Truly Random Seed

Pseudo-RandomGenerator

……………Lots of pseudo-random bits……………

deterministic

procedure

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Pseudo-Random?Pseudo-Random?• What are “good” pseudo-random bits?

• Statistical tests?

• Linear Congruential Generator(a,b,m,y0): yn=ayn-1+b (mod m) passes lots of tests.

• Insecure in practice!

• Need definition that guarantees security.

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A.I. Turing TestA.I. Turing Test

108

Cryptographic Turing TestCryptographic Turing Test

?

Our SystemPerfectlySecureSystem

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Def. for PRGDef. for PRG

?

Truly RandomBits

Random Seed

PRG

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Is it good enough?Is it good enough?• Consider Encryption Scheme, secure if use truly random bits.

• Can we use Encryption w/PRG?

• Consider any poly-time attacker s.t.:

• Breaks Encryption w/PRG

• Fails vs. Encryption w/true random bits

Encryption + Attacker = Distinguisher for PRG.

Contradiction.

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Reductions and SecurityReductions and Security• Want: Assumption Security

• How to prove? Use Contrapositive: Successful Attack Break Assumption

Must Give Reduction!

1 concurrency and zero-knowledge protocols amit sahai mit laboratory for computer science

Documents

epkxr y

true assertion

decryption of y np

assertion x

power of np np

probabilistic verifier

np statements

knowledge proofs cont