Detecting Inference Channels in Private Multimedia Data via Social Networks

Abstract : Indirect access to protected information has been one of the key challenges facing the international community for the last decade. Providing techniques to control direct access to sensitive information remain insufficient against inference channels established when legitimate data reveal classified facts hidden from unauthorized users. Several techniques have been proposed in the literature to meet indirect access prevention. However, those addressing the inference problem when involving multimedia objects (images, audio, video, etc.) remain few and hold several drawbacks. In essence, the complex structure of multimedia objects makes the fact of detecting indirect access a difficult task. In this paper, we propose a novel approach to detect possible inference channels established between multimedia objects representing persons by combining social network information with unmasked content of multimedia objects. Here, we present the techniques used to map the content of social networks to the set of multimedia objects at hand. We also provide an MiD function able to determine whether an unmasked multimedia object combined with data from the social network infers a sensitive multimedia object.
Type de document :
Communication dans un congrès
Data and Applications Security XXIII, 23rd Annual IFIP WG 11.3 Working Conference, Jul 2009, Montreal, Canada. Springer 2009 Lecture Notes in Computer Science, 2009, 〈http://dx.doi.org/10.1007/978-3-642-03007-9_14〉
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https://hal.archives-ouvertes.fr/hal-01094103
Contributeur : Richard Chbeir <>
Soumis le : jeudi 11 décembre 2014 - 16:18:37
Dernière modification le : samedi 14 juillet 2018 - 01:06:31

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  • HAL Id : hal-01094103, version 1

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Bechara Al Bouna, Richard Chbeir. Detecting Inference Channels in Private Multimedia Data via Social Networks. Data and Applications Security XXIII, 23rd Annual IFIP WG 11.3 Working Conference, Jul 2009, Montreal, Canada. Springer 2009 Lecture Notes in Computer Science, 2009, 〈http://dx.doi.org/10.1007/978-3-642-03007-9_14〉. 〈hal-01094103〉

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