Online Negative Databases View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2004

AUTHORS

Fernando Esponda , Elena S. Ackley , Stephanie Forrest , Paul Helman

ABSTRACT

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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal{NP}$\end{document}-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 described, which allow on-line creation and updates of negative databases, and future challenges are discussed. More... »

PAGES

175-188

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-30220-9_14

DOI

http://dx.doi.org/10.1007/978-3-540-30220-9_14

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018554980


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