Classification Encryption via Compressed Permuted Measurement Matrices

Abstract : In this paper we present an efficient encryption system based on Compressive Sensing for topic detection and classification in Twitter. The proposed method first employs Joint Complexity to perform topic detection. Then based on the spatial nature of the data, we apply the theory of Compressive Sensing to perform classification from a small number of random sample measurements. The breakthrough of the method is the encryption based on the permutation of measurements which are generated when solving the classification optimization problem. The experimental evaluation with real data from Twitter presents the robustness of the encryption accuracy, without using a specific cryptographic layer, while maintaining a low computational complexity.
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Communication dans un congrès
IEEE International Workshop on Security and Privacy in Big Data (BigSecurity), INFOCOM , Apr 2016, San Francisco, United States. 2016
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https://hal-polytechnique.archives-ouvertes.fr/hal-01272520
Contributeur : Dimitrios Milioris <>
Soumis le : jeudi 21 avril 2016 - 16:16:18
Dernière modification le : jeudi 9 février 2017 - 15:06:00
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  • HAL Id : hal-01272520, version 1

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Dimitrios Milioris. Classification Encryption via Compressed Permuted Measurement Matrices. IEEE International Workshop on Security and Privacy in Big Data (BigSecurity), INFOCOM , Apr 2016, San Francisco, United States. 2016. 〈hal-01272520〉

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