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Computational Forensic Engineering Techniques for Intellectual Property Protection

Explore the innovative approach of computational forensic engineering as an alternative to watermarking for protecting intellectual property. Learn how intrinsic properties analysis can deduce production processes without embedded information, providing zero overhead. Discover the goal of problem definition, foundational development, and practical demonstration. Benefit from solutions for isomorphic problem variants, feature extraction, clustering, and validation algorithms. Enhance your understanding of clustering, validation, Boolean satisfiability algorithms, and experimental results in forensic engineering applications.

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Computational Forensic Engineering Techniques for Intellectual Property Protection

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  1. 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. 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. 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 Watermarking vs. Forensic

  4. 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. . . Isomorphic problem variants of P Feature Extraction Clustering Validation Algorithm 1 Solution provided for each problem instance P and algorithm A Algorithm 2 Algorithm N Generic Approach: Data Collection Data Collection Original Problem Instance P Original Problem Instance P Isomorphic problem variants of P Perturbations Perturbations Algorithm 1 Solution provided for each problem instance P and algorithm A Algorithm 2 Algorithm N

  6. Feature Extraction Clustering Validation Generic Approach: Feature Extraction Data Collection • Extract property information from solutions • Identify Relevant Properties • Quantify Relevant Properties • Develop Fast Algorithm for Property Extraction

  7. Feature Extraction Clustering Validation Generic Approach: Clustering Data Collection • Partitioning of n-dimensional space • NP-complete problem

  8. Feature Extraction Clustering Validation Generic Approach: Clustering Data Collection

  9. Feature Extraction Clustering Validation Generic Approach: Validation Data Collection • Estimation and Validation Techniques • Nonparametric Statistical Techniques • Resubstitution

  10. 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. 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. Boolean Satisfiability Algorithms • GSAT (Selman ‘92) • Iterative Improvement • Variable oriented • Initial random assignment • Maximize satisfied number of clauses by flipping initial assignment

  13. Boolean Satisfiability Algorithms • Maximum Variable Benefit • Constructive • Variable oriented • Weighted clause appearance

  14. Boolean Satisfiability Properties

  15. Experimental Results • Boolean Satisfiability • NTAB, GSAT, Rel_SAT_rand • % of non-important variables • Ratio of true assigned variables

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

  17. Ratio of True Variables

  18. Experimental Results: SAT

  19. Experimental Results: Graph Coloring

  20. Forensic Engineering Applications • Intellectual Property Protection • Efficient Algorithm Selection • Algorithm Tuning • Instance Partitioning • Benchmark Selection

  21. Advancements • 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. Non-important Variables weighted average of “short ”clauses

  23. Clausal Stability weighted average of “short ”clauses

  24. Conclusion • Intrinsic Information Hiding • Attractive IPP technique • Alternative applications • In search for new applications and new techniques

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