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Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi,...

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Soft-Covering Exponent Paul Cuff and Semih Yagli
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Page 1: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Soft-Covering ExponentPaul Cuff and Semih Yagli

Page 2: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Soft Covering

- Opposite of Reliable Decoding - What happens when rate is too high?

Noise

X1

Y1

Noise

X2

Y2

Noise

X3

Y3

Noise

X4

Y4

i.i.d.

i.i.d.

Codebook

i.i.d.?

Page 3: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Output Distribution

PY

PY|C

x(1) x(2) x(3)x(4)x(5)

Page 4: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Gaussian Example

Page 5: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Gaussian Example

Page 6: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Soft Covering (origin)

Theorem 6.3 of Wyner’s Common Information Paper

R > I(X;Y) produces the phenomenon

Page 7: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Various Names

Covering [Ahlswede, Winter, Wilde]

Sampling Lemma [Winter]

Resolvability [Han, Verdu]

Cloud Mixing

Page 8: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Applications

Security and Privacy

e.g. wiretap channel

Encoder analysis

Common Information

Page 9: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Main Result

Page 10: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Previous lower bounds

Notable bounds by Hayashi, Han, Verdu, Cuff

Best previous:

Page 11: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

TV vs KL-divergence

Inspired by [Parizi, Telatar, Merhav 17]

Solved exponent for KL-divergence

Page 12: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Features of TV

Accepted standard for cryptography

Tractable multi-stage encoder analysis

Stronger concentration result

Page 13: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

New Features of TV Proof

“Typical set” approach is not optimal

Poisson codebook size used in converse

(Taylor expansion not possible for absolute value)

Page 14: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Equivalence

Carefully swap min’s and max’s

Massage

[R�D(QXY kPXQY )]+ = max�2[0,1]

�(R�D(QXY kPXQY ))<latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit>

Page 15: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Proof of Error Upper Bound

Page 16: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Illustration of codebook approximation

Ynyn

Page 17: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Conditional Type

0 0 0 0 1 0 0 00 1 0 1 1 1 0 11 1 1 1 1 0 0 0

yn

xn same conditional type

Page 18: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Polynomial Number of Types

Length-n binary types

fraction of 1’s0 1/n 2/n 1

Length-n ternary types

fraction of 1’s

fraction of 2’s

1

1

Page 19: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Key Quantities

Page 20: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Bound each type in one of two ways

Page 21: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Proof of Error Lower Bound

Page 22: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Poisson Trick

Let the number of codewords M be Poisson distributed

Take care of this assumption at the end

Page 23: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Another Look

Need independence for different types

Page 24: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

A Simple Lemma{Xi} independent

E[Xi]=0

E�����X

i

Xi

����� � maxi

E |Xi|<latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit>

Page 25: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Initial Details

Page 26: Soft-Covering Exponent - Princeton Universitycuff/publications/cuff_ita_2019.pdfInspired by [Parizi, Telatar, Merhav 17] Solved exponent for KL-divergence. Features of TV Accepted

Poisson to Fixed Codebook

Codebook Size (M)

codebook size PMFexponentially small

(still too big to be bad)

μ


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