Integrating Multi-Modal Circuit Features within an Efficient Encryption System

The problem of the incorporation of pattern features with unusual distributions is well known within pattern recognition systems even if not easily addressed. The problem is more acute when features are derived from characteristics of given integrated electronic circuits. The current paper introduces novel efficient techniques for normalising sets of features which are highly multi-modal in nature, so as to allow them to be incorporated within a single encryption key generation system based primarily on measured hardware characteristics. The utility of the proposed system lies in the observation that the need for data sent to and from remote network nodes to be secure and verified is substantial. Security can be improved by using encryption techniques based on keys, which are based on unique properties of the individual nodes within the network. This will serve both to minimize the need for key storage and sharing as well as to validate the initiator node of a message.

[1]  Hao Feng,et al.  Private key generation from on-line handwritten signatures , 2002, Inf. Manag. Comput. Secur..

[2]  W. Gareth J. Howells,et al.  Novel Techniques for Ensuring Secure Communications for Distributed Low Power Devices , 2006, First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06).

[3]  Toshiro Kubota,et al.  Massively parallel networks for edge localization and contour integration-adaptable relaxation approach , 2004, Neural Networks.

[4]  Thomas Sandholm,et al.  Policy administration control and delegation using XACML and Delegent , 2005, The 6th IEEE/ACM International Workshop on Grid Computing, 2005..

[5]  Tsuhan Chen,et al.  Biometrics-based cryptographic key generation , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[6]  Erfu Yang,et al.  ESPACENET: A Framework of Evolvable and Reconfigurable Sensor Networks for Aerospace–Based Monitoring and Diagnostics , 2006, First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06).

[7]  W. Gareth J. Howells,et al.  Normalizing Discrete Circuit Features with Statistically Independent values for incorporation within a highly Secure Encryption System , 2007, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007).

[8]  Cordelia Schmid,et al.  Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval , 2004, International Journal of Computer Vision.