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On-line Negative Databases
Fernando Esponda, Elena S. Ackley, Stephanie Forrest and Paul Helman

The benefits of negative detection for obscuring information are explored in the context of Artificial Immune Systems (AIS). AIS based on string matching have the potential for an extra security feature in which the “normal” profile of a system is hidden from its possible hijackers. Even if the model of normal behavior falls into the wrong hands, reconstructing the set of valid or “normal” strings is an NP-hard problem. The data-hiding aspects of negative detection are explored in the context of an application to negative databases. Previous work is reviewed describing possible representations and reversibility properties for privacy-enhancing negative databases. New algorithms are presented which allow on-line creation, updates and clean-up of negative databases, some experimental results illustrate the impact of these operations on the size of the negative database. Finally some future challenges are discussed.

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