# ppt

## Transcription

ppt
```Revisions to the Spectral Test and the
Lempel-Ziv Compression Test in the NIST
Statistical Test Suite
National Institute of Information and
Communications Technology, JAPAN
Song-Ju Kim and Ken Umeno
（ChaosWare Inc.）


It is well known that the NIST Statistical
Test Suite was used in the evaluation of
the AES candidate algorithms.
It is also world-widely used by external
audiences in the evaluation of their
Pseudo Random Number Generators.
The NIST Statistical Test Suite
“A Statistical Test Suite for Random and
Pseudorandom Number Generators for
Cryptographic Applications”
National Institute of Standards and Technology
(2001)
http://csrc.nist.gov/rng/
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Test Name
Frequency
Block Frequency
Runs
Longest Run
Binary Matrix Rank
Discrete Fourier Transform
Non-overlapping Template Matching
Overlapping Template Matching
Universal
Lempel Ziv Compression
Linear Complexity
Serial
Approximate Entropy
Cumulative Sums
Random Excursions
Random Excursions Variant
OUTLINE
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
On the NIST Statistical Test Suite
Test Results (AES, SHA-1, and MUGI)
Checking of the Uniformity of P-values
Corrections to the Spectral (DFT) Test
Corrections to the LZC Test
Summary
The test procedure

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A set of sequences, each of length n, is
produced from the selected generator.
Each statistical test evaluates the sequence
and returns one or more P-values.
If the P-value ≥ α(=0.01), then we call the
sequence “success”.
1. Checking of the success rate.
2. Checking of the uniformity of the
distribution of P-values.
What is p-value?
P-value:
the probability that a perfect
random number generator would
have produced a sequence less
random than the sequence that are
tested.
1. The checking of the success rate

The range of acceptable proportions:
1  3
 (1   )
m
※ (μ±3σ)/m : 99.73% range of binomial distribution,
where μ= m (1 – α) and σ= m α(1- α).
α=0.01: significance level
Success Rate (Example)
Key 1
Key 4
2. The checking of the uniformity of
the P-values distribution
The interval [0,1] is divided into 10 sub intervals, and the p-values that
lie within each sub-intervals are counted (F i).
p-value of p-values: IGMC( 9 / 2, χ2 / 2 )

1
t n 1
dt
where IGMC(n, x) =
e
t

 ( n) x
and
The test passes if p-value of p-values ≥ 0.0001
 
2
10
i 1
(F i 
m
10
m
10
2
)
Uniformity of p-values (Example)
Key 1 (fail)
Key 4 (pass)
The parameters we used
TEST NAME
Block Frequency
Template Matching
Universal
(Initialization Steps)
Linear Complexity
Serial
Approximate Entropy
BLOCK LENGTH
20000
9
7
(1280)
500 (5000)
10
10
6
n=10,
α=0.01,
1000 samples
10 keysх1000 samplesх10^6 (sequence length)
total 10^10 bit
Test Results AES (OFB)
Key
１
２
３
４
５
６
７
８
９
１０
Success Rate
pass
pass
REX
pass
NOTM(2)
CUSUM
NOTM, OTM
pass
pass
pass
Uniformity
pass
pass
pass
pass
Lempel-Ziv
Lempel-Ziv
pass
pass
Lempel-Ziv
Lempel-Ziv
Test Results SHA-1
Key
１
２
３
４
５
６
７
８
９
１０
Success Rate
pass
pass
NOTM(2)
NOTM(2)
pass
NOTM, REX, REXV
NOTM(2)
NOTM
pass
pass
Uniformity
pass
Lempel-Ziv
pass
FFT
Lempel-Ziv
pass
pass
pass
pass
Lempel-Ziv
Test Results MUGI
Key
１
２
３
４
５
６
７
８
９
１０
Success Rate
NOTM
pass
Lempel-Ziv
pass
NOTM
pass
pass
pass
NOTM
pass
Uniformity
pass
Lempel-Ziv
Lempel-Ziv
pass
pass
pass
pass
pass
pass
FFT
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If we focus on the uniformity of Pvalues, only the DFT test and LZC test
are failed frequently.
If we choose the sample size m greater
than 10000, we cannot find any PRNG
that pass these two test.
P-value of P-values (SHA-1)
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These distributions of P-values indicates
a apparent deviation from randomness
although we use a well-known good
PRBG (SHA-1)
This observation suggests that the test
settings in these two tests are not
accurate.
The DFT test
test description (NIST document)
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The zeros and ones of the input sequence are
converted to values of -1 and +1.
Apply a DFT on X to produce: S=DFT(X).
Calculate M=modulus(S’), where S’ is the substring
consisting of the first n/2 elements in S.
Compute T= 3n : the 95% peak height threshold
value.
Compute N0 = 0.95n/2.
Compute N1 = the actual observed number of
peaks in M that are less than T.
N
| d |
Compute P-value =
N
d
erfc

 2
1
0
n(0.95)(0.05) / 2
The probability distribution
(SHA-1)
2.995732274n
300,000
samples
3n
npq

4
npq

2
The LZC test
test description (NIST document)
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Parse the sequence into consecutive,
disjoint and distinct words that will form a
“dictionary” of words in the sequence.
ex. 0|1|00|01|000|11|011|
Compute P-value =
  W 
obs 
1

erfc
2 
2


 2 
The probability distribution
(SHA-1)
  69588.09


2
L
2
R
 75.574336518
 72.42178447
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Despite the best fitting of the
distribution, the uniformity of P-values
cannot be improved.
This is because the distribution of the
number of words is too narrow.
In other words, a variety of the
appeared P-values is limited.
The effect of discreteness
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Because the variety of appeared Pvalues is too scarce in centered bins, we
never get the uniformity of P-values in
this situation.
The histogram of P-values always has
some biases even if we use good PRNG.
However, these biases are always the
same if we use good PRNG.
Checking of Uniformity (LZ)
 
2
10
i 1
(F i 
m
2
)
10
( F i  m S i)
i 1
m Si
 
2
10
m
10
S
S
S
S
S
1
3
5
7
9
 0.1097085,
 0.1076910,
 0.1369235,
 0.0858035,
 0.1028565,
S
S
S
S
S
2
4
6
8
2
 0.0791270,
 0.0844650,
 0.0911150,
 0.1098615,
10
 0.0924485.
P-value of P-values (before)
P-value of P-values (after)
Summary
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We corrected two points for DFT test.
(1) the threshold T
3n
(2) the variance of the theoretical distribution
npq
2
npq
2


 4
2
We corrected two points for LZ test.
(1) setting of standard distribution (asymmetric) which has no
algorithm dependence.
(2) re-definition of the uniformity of P-values.


2.995732274n
```