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Statistical Forensic Engineering Statistical Forensic Engineering Techniques for Intellectual Property Techniques for Intellectual Property

ProtectionProtection

Jennifer L. WongJennifer L. Wong††,,

Darko Kirovski*, Miodrag PotkonjakDarko Kirovski*, Miodrag Potkonjak††

††UCLA Computer Science Department UCLA Computer Science Department

University of California, Los Angeles, CAUniversity of California, Los Angeles, CA

*Microsoft Research, Redmond, WA*Microsoft Research, Redmond, WA

IHW, April 2001IHW, April 2001

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Computational Forensic EngineeringComputational Forensic Engineering

• Alternative to watermarking for IPP

• Analyze intrinsic properties to deduce process of production

• Resolves legacy issue

• Zero overhead

• Goal: Define problem, develop sound foundations, demonstrate in practice

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Watermarking vs. ForensicWatermarking vs. Forensic

Forensic• Resolves legacy issue• No info embedded• Zero overhead• Many applications

Watermarking• IPP only• Embed information• Control level of

information• Fingerprinting• Fast / Easy to detect

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Related WorkRelated Work

• Java Byte Codes (Baker &Manber 98)• Software Obfuscation (Collberg 99)

• Reverse Engineering(Kuhn &Anderson 97, Maher 97)

• Information Recovery (Gutmann 96)– Disk & Semi conductor memory

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Generic Approach: Data Collection Generic Approach: Data Collection Data

CollectionFeature

ExtractionClustering Validation

Original Problem

Instance PPerturbations

Solution provided foreach problem

instance P and algorithm A

Algorithm 1

Algorithm 2

Algorithm N

..

Isomorphicproblem

variants of P

Original Problem

Instance PPerturbations

Isomorphicproblem

variants of P

Algorithm 1

Algorithm 2

Algorithm N

Solution provided foreach problem

instance P and algorithm A

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Generic Approach: Feature Extraction Generic Approach: Feature Extraction

• Extract property information from solutions

• Identify Relevant Properties

• Quantify Relevant Properties

• Develop Fast Algorithm for Property Extraction

Data Collection

FeatureExtraction

Clustering Validation

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Generic Approach: Clustering Generic Approach: Clustering

• Partitioning of n-dimensional space

• NP-complete problem

Data Collection

FeatureExtraction

Clustering Validation

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Generic Approach: ClusteringGeneric Approach: Clustering

P2

P1

P2

P1

Data Collection

FeatureExtraction

Clustering Validation

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Generic Approach: Validation Generic Approach: Validation

• Estimation and Validation Techniques

• Nonparametric Statistical Techniques– Resubstitution

Data Collection

FeatureExtraction

Clustering Validation

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Boolean Satisfiability PropertiesBoolean Satisfiability Properties

• Percentage of Non-Important Variables

• Ratio of True Assigned Variables vs. Total Number of Variables in a Clause

• Ratio of Coverage using True and False Appearance of a Variable

• Clausal Stability

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Boolean Satisfiability AlgorithmsBoolean Satisfiability Algorithms

• Max/Min– Constructive– Clause oriented– Maximally constrained

• Small clauses

• Variable: appearance ratio not in favor

– Minimally constraining• Assign var who does the least amount of damage

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Boolean Satisfiability AlgorithmsBoolean Satisfiability Algorithms

• GSAT (Selman ‘92)– Iterative Improvement– Variable oriented– Initial random assignment– Maximize satisfied number of clauses by

flipping initial assignment

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Boolean Satisfiability AlgorithmsBoolean Satisfiability Algorithms

• Maximum Variable Benefit– Constructive – Variable oriented– Weighted clause appearance

n2

1

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Boolean Satisfiability PropertiesBoolean Satisfiability PropertiesMax/Min GSAT Max

Benefit

% of non-important variables

Small Largest Large

Normalized ratio of true satisfied

variablesMedium Medium Small

Ratio of coverage using true and false

appearances

Large false appearances

Large true appearances

Large true appearances

Max/Min GSAT Max Benefit

% of non-important variables

Small Largest Large

Max/Min GSAT Max Benefit

% of non-important variables

Small Largest Large

Normalized ratio of true satisfied

variablesMedium Medium Small

Ratio of coverage using true and false

appearances

Large false appearances

Large true appearances

Large true appearances

Max/Min GSAT Max Benefit

% of non-important variables

Small Largest Large

Normalized ratio of true satisfied

variablesMedium Medium Small

Ratio of coverage using true and false

appearances

Large false appearances

Large true appearances

Large true appearances

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Experimental ResultsExperimental Results

• Boolean Satisfiability– NTAB, GSAT, Rel_SAT_rand– % of non-important variables– Ratio of true assigned variables

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Experimental Results: Boolean Satisfiability -Experimental Results: Boolean Satisfiability -% of Non-Important Variables% of Non-Important Variables

0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.850

10

20

30

40

50

60

70

80

90

100percent_NIV: NTAB(blue), WALKSAT(red), RELSATR(green)

Value

Fre

qu

en

cy

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Ratio of True VariablesRatio of True Variables

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

1000

2000

3000

4000

5000

6000clausal_truth_percent: NTAB

Value

Fre

qu

en

cy

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

500

1000

1500

2000

2500

3000

3500

4000

4500clausal_truth_percent: WALKSAT

Value

Fre

qu

en

cy

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Experimental Results: SATExperimental Results: SAT

WalkSAT RelSATR NTAB

WalkSAT 992992 5 3

RelSATR 6 990990 4

NTAB 0 2 998998

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Experimental Results: Graph ColoringExperimental Results: Graph Coloring

Bkdsat Maxis Tabu Itrgrdy

Bkdsat 998998 2 0 0

Maxis 3 993993 0 4

Tabu 1 0 995995 4

Itrgrdy 1 2 0 997997

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Forensic Engineering ApplicationsForensic Engineering Applications

• Intellectual Property Protection

• Efficient Algorithm Selection

• Algorithm Tuning

• Instance Partitioning

• Benchmark Selection

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AdvancementsAdvancements

• Properties of an Instance– Clause difficulty– Variable appearance ratio– Likelihood of a constraint to be satisfied

• Calibration of Properties– Instance properties: classify the instances– Solution properties: calibrated per instance →

proper perspective for the algorithm classification

• Classification of “Not seen algorithm”

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Non-important VariablesNon-important Variablesw

eigh

ted

aver

age

of “

shor

t ”cl

ause

s

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Clausal StabilityClausal Stabilityw

eigh

ted

aver

age

of “

shor

t ”cl

ause

s

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ConclusionConclusion

• Intrinsic Information Hiding

• Attractive IPP technique

• Alternative applications

• In search for new applications and new techniques