The semipublic encryption for visual cryptography using q'tron neural networks

Authors:
Tai-Wen Yue;Suchen Chiang
Affiliations:
Computer Science and Engineering, Tatung University, Taiwan;Computer Science and Engineering, Tatung University, Taiwan
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

Citing 2
Cited 0

Visual cryptography for general access structures

Information and Computation
The General Neural-Network Paradigm for Visual Cryptography

IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper proposes the semipublic encrypting scheme for visual cryptography using the Q'tron neural-network (Q'tron NN) model This encrypting scheme hides only the true secret from the public That is, the pictorial meaning appearing in a public share describes the public information in a document while leaving its confidential part undisclosed A piece of confidential information is retrievable if and only if a right user share is available The method to construct the Q'tron NN to fulfill the aforementioned scheme will be investigated An application that uses the scheme for key distribution in a public area will be demonstrated.