Electrical & Computer Engineering Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1658

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Author: search.filters.author.Potkonjak, MiodragSubject: search.filters.subject.intellectual property protection (IPP)

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    Fingerprinting Intellectual Property Using Constraint-Addition
    (IEEE, 2000-06) Qu, Gang; Potkonjak, Miodrag
    Recently, intellectual property protection (IPP) techniques attracted a great deal of attention from semiconductor, system integration and software companies. A number of watermarking-based techniques have been proposed for IPP. One of the key limitations of watermarking is that it does not facilitate tracing of illegally resold intellectual property (IP). Fingerprinting resolves this problem by providing each customer with a unique instance of functionally identical IP. We propose a general technique which enables fingerprinting at all level of design process and is applicable to an arbitrary optimization step. In particular, we address the following fingerprinting problem: How to generate a large number of high quality solution for a given optimization problem by solving the initial problem only once. In addition we also discuss how to select a subset of k solutions from the pool of n solutions so that the solutions are maximally different. In order to make our discussion concrete we focus on a single NP-complete problem - graph coloring. We test the new fingerprinting on a number of standard benchmarks. Interestingly, while on random graphs it is relatively difficult to produce a large number of solutions without nontrivial quality degradation, on all real-life compilation graphs we are able to generate millions of solution which are all optimal.
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    Fair Watermarking Techniques
    (IEEE, 2000-01) Qu, Gang; Wong, Jennifer L.; Potkonjak, Miodrag
    Many intellectual property protection (IPP) techniques have been proposed. Their primary objectives are providing convincible proof of authorship with least degradation of the quality of the intellectual property (IP), and achieving robustness against attacks. These are also well accepted as the most important criteria to evaluate different IPP techniques. The essence of such techniques is to limit the solution space by embedding signatures as constraints. One key issue that should be addressed but has not been discussed is the fairness of the techniques: what is the quality of the solution subspace for different signatures, that is, how large the solution subspace is (uniqueness), and how difficulty it is to get a solution from such subspace (hardness)? In this paper, we introduce fairness as one of the metrics for good IPP techniques and post the challenge problem of how to design fair watermarking techniques. We claim that all fair techniques have to be instanceoriented and due to the complexity of the problem itself, we propose an approach that utilizes the statistical information of the problem instance. We use the satisfiability (SAT) problem as an example to illustrate how fairness could be achieved. We make the observation that the unfairness of the previous watermarking techniques comes from the global embedding of the signature and propose fair watermarking techniques. We test the uniqueness and hardness on a model with full knowledge of the solution and real life benchmarks as well. The experimental results show fairness can be achieved.
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    Effective Iterative Techniques for Fingerprinting Design IP
    (IEEE, 1999-06) Caldwell, Andrew E.; Choi, Hyun-Jin; Kahng, Andrew B.; Mantik, Stefanus; Potkonjak, Miodrag; Qu, Gang; Wong, Jennifer L.
    While previous watermarking-based approaches to intellectual property protection (IPP) have asymmetrically emphasized the IP provider’s rights, the true goal of IPP is to ensure the rights of both the IP provider and the IP buyer. Symmetric fingerprinting schemes have been widely and effectively used to achieve this goal; however, their application domain has been restricted only to static artifacts, such as image and audio. In this paper, we propose the first generic symmetric fingerprinting technique which can be applied to an arbitrary optimization/synthesis problem and, therefore, to hardware and software intellectual property. The key idea is to apply iterative optimization in an incremental fashion to solve a fingerprinted instance; this leverages the optimization effort already spent in obtaining a previous solution, yet generates a uniquely fingerprinted new solution. We use this approach as the basis for developing specific fingerprinting techniques for four important problems in VLSI CAD: partitioning, graph coloring, satisfiability, and standard-cell placement. We demonstrate the effectiveness of our fingerprinting techniques on a number of standard benchmarks for these tasks. Our approach provides an effective tradeoff between runtime and resilience against collusion.