1. {jwong,darko,miodrag}@cs.ucla.edu Statistical Forensic Engineering Techniques for Intellectual Property Protection Jennifer L. Wong †, Darko Kirovski*, Miodrag Potkonjak † † UCLA Computer Science Department University of California, Los Angeles, CA *Microsoft Research, Redmond, WA IHW, April 2001

2. {jwong,darko,miodrag}@cs.ucla.edu Computational 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

3. {jwong,darko,miodrag}@cs.ucla.edu Watermarking 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

4. {jwong,darko,miodrag}@cs.ucla.edu Related 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

5. {jwong,darko,miodrag}@cs.ucla.edu Generic Approach: Data Collection Data Collection Feature Extraction ClusteringValidation Original Problem Instance P Perturbations Solution provided for each problem instance P and algorithm A Algorithm 1 Algorithm 2 Algorithm N.. Isomorphic problem variants of P Original Problem Instance P Perturbations Isomorphic problem variants of P Algorithm 1 Algorithm 2 Algorithm N Solution provided for each problem instance P and algorithm A

6. {jwong,darko,miodrag}@cs.ucla.edu Generic Approach: Feature Extraction Extract property information from solutions Identify Relevant Properties Quantify Relevant Properties Develop Fast Algorithm for Property Extraction Data Collection Feature Extraction ClusteringValidation

7. {jwong,darko,miodrag}@cs.ucla.edu Generic Approach: Clustering Partitioning of n-dimensional space NP-complete problem Data Collection Feature Extraction ClusteringValidation

8. {jwong,darko,miodrag}@cs.ucla.edu Generic Approach: Clustering Data Collection Feature Extraction ClusteringValidation

9. {jwong,darko,miodrag}@cs.ucla.edu Generic Approach: Validation Estimation and Validation Techniques Nonparametric Statistical Techniques –Resubstitution Data Collection Feature Extraction ClusteringValidation

10. {jwong,darko,miodrag}@cs.ucla.edu Boolean 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

11. {jwong,darko,miodrag}@cs.ucla.edu Boolean 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

12. {jwong,darko,miodrag}@cs.ucla.edu Boolean Satisfiability Algorithms GSAT (Selman ‘92) –Iterative Improvement –Variable oriented –Initial random assignment –Maximize satisfied number of clauses by flipping initial assignment

13. {jwong,darko,miodrag}@cs.ucla.edu Boolean Satisfiability Algorithms Maximum Variable Benefit –Constructive –Variable oriented –Weighted clause appearance

14. {jwong,darko,miodrag}@cs.ucla.edu Boolean Satisfiability Properties Max/MinGSATMax Benefit % of non-important variables SmallLargestLarge Normalized ratio of true satisfied variables Medium Small Ratio of coverage using true and false appearances Large false appearances Large true appearances Max/MinGSATMax Benefit % of non-important variables SmallLargestLarge Max/MinGSATMax Benefit % of non-important variables SmallLargestLarge Normalized ratio of true satisfied variables Medium Small Ratio of coverage using true and false appearances Large false appearances Large true appearances Max/MinGSATMax Benefit % of non-important variables SmallLargestLarge Normalized ratio of true satisfied variables Medium Small Ratio of coverage using true and false appearances Large false appearances Large true appearances

15. {jwong,darko,miodrag}@cs.ucla.edu Experimental Results Boolean Satisfiability – NTAB, GSAT, Rel_SAT_rand – % of non-important variables – Ratio of true assigned variables

16. {jwong,darko,miodrag}@cs.ucla.edu Experimental Results: Boolean Satisfiability - % of Non-Important Variables

17. {jwong,darko,miodrag}@cs.ucla.edu Ratio of True Variables

18. {jwong,darko,miodrag}@cs.ucla.edu Experimental Results: SAT WalkSATRelSATRNTAB WalkSAT99253 RelSATR 69904 NTAB 02998

19. {jwong,darko,miodrag}@cs.ucla.edu Experimental Results: Graph Coloring BkdsatMaxisTabuItrgrdy Bkdsat998200 Maxis399304 Tabu109954 Itrgrdy120997

20. {jwong,darko,miodrag}@cs.ucla.edu Forensic Engineering Applications Intellectual Property Protection Efficient Algorithm Selection Algorithm Tuning Instance Partitioning Benchmark Selection

21. {jwong,darko,miodrag}@cs.ucla.eduAdvancements 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”

22. {jwong,darko,miodrag}@cs.ucla.edu Non-important Variables weighted average of “short ”clauses

23. {jwong,darko,miodrag}@cs.ucla.edu Clausal Stability weighted average of “short ”clauses

24. {jwong,darko,miodrag}@cs.ucla.edu Conclusion Intrinsic Information Hiding Attractive IPP technique Alternative applications In search for new applications and new techniques