1 the complexity of lattice problems oded regev, tel aviv university amsterdam, may 2010 (for more...

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The Complexity of Lattice Problems

Oded Regev, Tel Aviv University

Amsterdam, May 2010

(for more details, see LLL+25 survey)

Lattice

v1 v2

0

2v1v1+v2 2v2

2v2-v1

2v2-2v1

• For vectors v1,…,vn in Rn we define the lattice generated by them as

L={a1v1+…+anvn | ai integers}

• We call v1,…,vn a basis of L

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• Lattice problems are among the richest problems in complexity theory, exhibiting a wide range of behaviors:– Some problems are in P (as shown by LLL)– Some problems are NP-hard– Some problems are not known to be in P, but

believed not to be NP-hard• As a rule of thumb, ‘algebraic’ problems

are easy; ‘geometric’ problems are hard

Lattices from a Computational Complexity Point of View

4• GapSVP: Given a lattice, decide if the length of the shortest vector is:– YES: less than 1– NO: more than

Shortest Vector Problem (SVP)

0

v2

v1

5

• GapCVP: Given a lattice and a point v, decide if the distance of v from the lattice is:– YES: less than 1– NO: more than

• GapSVP is not harder than GapCVP [GoldreichMicciancioSafraSeifert99]

• Both problems are clearly in NP (for any )

Closest Vector Problem (CVP)

0

v2

v1

v

6

• Polytime algorithms for gap 2n loglogn/logn

[LLL82,Schnorr87,AjtaiKumarSivakumar02]• Hardness is known for:

– GapCVP: nc/loglogn [vanEmdeBoas81…,DinurKindlerRazSafra03]

– GapSVP: 1 in l1 [vanEmdeBoas81] 1 [Ajtai96]

2 [Micciancio98] 2^(log½-εn) [Khot04]

nc/loglogn [HavivR07]

Known Results

2n loglogn/logn

P

1

NP-hard

nc/loglogn?n

Cryptography[Ajtai96,AjtaiDwork97

…]

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Known ResultsLimits on Inapproximability

• GapCVPn 2 NP∩coNP [LagariasLenstraSchnorr90,

Banaszczyk93]• GapCVPn/logn 2 NP∩coAM

[GoldreichGoldwasser98]• GapCVPn 2 NP∩coNP [AharonovRegev04]1 2n loglogn/logn

NP-hard P

nn

NP∩coNPNP∩coAMNP∩coNP

nc/loglogn

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What’s ahead?

1. GapCVPn/logn 2 NP∩coAM [GoldreichGoldwasser98]

2. GapCVPn 2 NP∩coNP [AharonovRegev04]

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What’s ahead?

1. GapCVPn/logn 2 coAM [GoldreichGoldwasser98]

2. GapCVPn 2 coNP [AharonovRegev04]

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Chapter I

GapCVPn in coAM

[GoldreichGoldwasser98]

Chapter I

GapCVPn in coAM

[GoldreichGoldwasser98]

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Given:

- Lattice L (specified by a basis) - Point v

We want to:

Be convinced that v is far from L by interacting with an (all powerful) prover (using a constant number of rounds)

Our Goal

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The Idea

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Basic High-dimensional Geometry

• How big is the intersection of two balls of radius 1 in n dimensions whose centers are at distance apart?– When 2, balls disjoint– When =0, balls exactly overlap – When =0.1, intersection is exponentially

small– When =1/n, intersection is constant

fraction

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The Protocol

• Flip a fair coin– If heads, choose a random point in L+B– If tails, choose a random point in

L+B+v• Send the resulting point to the

prover• The prover is supposed to tell

whether the coin was heads of tails

(Can be implemented efficiently)

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Demonstration of Protocol

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Demonstration of Protocol

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Analysis

• If dist(v,L)>2 then prover can always answer correctly

• If dist(v,L)<1/n then with some constant probability, the prover has no way to tell what the coin outcome was– Hence we catch the prover cheating with

some constant probability

• This completes the proof

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Chapter II

GapCVPn in coNP

[AharonovR04]

Chapter II

GapCVPn in coNP

[AharonovR04]

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Given:

- Lattice L (specified by a basis) - Point v

We want:

A witness for the fact that v is far from L

Our Goal

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Overview

Step 1: Define fIts value depends on the distance from L:

– Almost zero if distance > n – More than zero if distance < log n

Step 2: Encode f Show that the function f has a short description

Step 3: Verifier Construct the NP verifier

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Step 1:

Define f

Step 1:

Define f

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The function fConsider the Gaussian:

Periodize over L:

Normalize by g(0):

Ly

yxexLxg2

)()(

2

)( xex

)0()()( gxgxf

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The function f (pictorially)

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f distinguishes between far and close vectors

(a) d(x,L)≥n f(x)≤2-Ω(n)

(b) d(x,L)≤logn f(x)>n-5

Proof: (a) [Banaszczyk93] (b) Not too difficult

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Step 2:

Encode f

Step 2:

Encode f

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The function f (again)

Ly

yxexg2

)(

)0()()( gxgxf

Let’s consider its Fourier transform !

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f ̂ is a probability distribution

LxZxwwL ,|*

Claim: f ̂ : L*R+ is a probability distribution on L*

g is a convolution of a Gaussian and δL

Proof:

..

*

0ˆ)(ˆ

22

wo

Lweewgw

Lx

*

2

2

)0()(ˆ)(ˆ

Lz

z

w

e

egwgwf

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f as an expectation

*

,2)(ˆ)(Lw

wxiewfxf

In fact, it is an expectation of a real variable between -1 and 1:

Chernoff

][ ,2ˆ

wxi

fweE

)],2[cos()( ˆ

wxExffw

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Encoding f

(Chernoff) This is true even pointwise!

)],2[cos()( ˆ

wxExffw

Pick W=(w1,w2,…,wN) with N=poly(n)

according to the f ̂ distribution on L*

N

jjNW wxxf

1

1 ),2cos()(

)()( xfxf W

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The Approximating Function

N

jjNW wxxf

1

1 ),2cos()(

(with N=1000 dual vectors)

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Interlude: CVPP

GapCVPP Solve GapCVP on a preprocessed lattice

(allowed infinite computational power, but before seeing v)

(ideas led to [MicciancioVoulgaris10]’s recent deterministic 2n algorithm for lattice problems)

Algorithm for GapCVPP: Prepare the function fW in advance; When given v, calculate fW(v).

Algorithm for GapCVPP(n/logn) (best known!)

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This concludes Step 2: Encode f

The encoding is a list W of vectors in L*fW(x) ≈ f(x)

This concludes Step 2: Encode f

The encoding is a list W of vectors in L*fW(x) ≈ f(x)

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Step 3:

NP Verifier

Step 3:

NP Verifier

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The Verifier (First Attempt)

Given input L,v, and witness W, accept iff 1. fW (v) < n-10, and

2. fW(x) > n-5 for all x within distance logn from L• This verifier is correct

• But: how to check (2) efficiently?

- First check that fW is periodic over L (true if W in L*)

- Then check that >n-5 around origin

• We don’t know how to do this for distance logn

• Instead, we do this for distance 0.01

0.01

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The Verifier (Second Attempt)

Given input L,v, and witness W, accept iff

1. fW (v) < n-10, and

2. w1,…,wN L*, and

3. 100

)(,,

2

2

u

Wn

x

xfux

2 implies that fW is periodic on L:

N

jjNW

n wyxyxfLyx1

1 ),2cos()(,,

N

jjjN wywx

1

1 ),2,2cos( )(xfW

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The Verifier (Second Attempt)

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

f W(x

)

0 .01-.01

1)0( Wf

0)0(

u

W

x

f

Given input L,v, and witness W, accept iff

1. fW (v) < n-10, and

2. w1,…,wN L*, and

3. 100

)(,,

2

2

u

Wn

x

xfux

3 implies that fW is at least 0.8 within distance 0.01 of the origin:

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The Final Verifier

NwwwW .......21

Given input L,v, and witness W, accept iff

1. fW (v) < n-10, and

2. w1,…,wN L*, and

3. ||WWT||<N where

N

jju

TTu

T wuuuWWWW1

211 ,maxmax

3 checks that in any direction the w’s are not too long:

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The Final Verifier Given input L,v, and witness W, accept

iff 1. fW (v) < n-10, and

2. w1,…,wN L*, and

3. ||WWT||<N where

NwwwW .......21

),2cos(,4)(

1

22

2

2

xwuwNx

xfj

N

jj

u

W

10044

,4)( 22

1

22

2

2

TTTN

jj

u

W WWN

uuWWN

uwNx

xf

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Conclusion and Open Questions

• Lattice problems with approximation factors >n are unlikely to be NP-hard– These are the problems used for crypto– Can we say anything about their

hardness?• Perhaps relate to hardness of other

problems, say factoring?• Extremely important question for crypto

• Can the containment in NP∩coNP be improved to (n/logn) or even below?

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Thanks!