Secure authentication from ambient audio: Difference between revisions

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In this implementation, similar recordings of ambient audio from mobile devices in proximity are utilised for the generation of a secure key.

An attacker that is farther away is not able to guess the same key with a recording of her own since the features in the ambient audio differ with location.

However, the robustness of the generated key is dependent on properties of ambient audio.

In previous studies, we experienced best pairing characteristics when devices are in an environment with a single loud audio source.

When environmental noise is, however, more ubiquitous and noisy, it is easier for an attacker to generate a key that is similar to that of the legitimate communication partners.

~~[[File:AudioSynch~~.~~png]]~~

In this project we will develop a mechanism to adapt the parameter of the underlying approach to generate secure to changing environmental parameters.

~~Indoor positioning still remains a challenging task since GPS does not work indoors and reliable.~~

[[File:AudioSynch.png]]

~~Furthermore, ubiquitously deployed alternatives are not available.~~

~~Recenty, localisation techniques based on radio frequency measurements have been proposed.~~

~~In this project, the student(s) will design and implement an indoor localisation system based on RF-signal strength fluctuation.~~

~~We will provide USRP software defined radio devices and further required hardware equipment to design an accurate RF-based indoor localisation system.~~

~~In the frame of the 2014 IPSN Indoor Localisation Competition~~ [~~http~~:~~//ipsn~~.~~acm.org/2014/content/pdf/IndoorLocation-CFC.pdf~~] ~~the designed system can be tested under realistic conditions.~~

~~A participation is highly appreciated and will be supported.~~

== Required Skills ==

* Natural curiosity, high motivation and a good sense of creativity :)

* ~~Programming skills in Python will~~ be ~~required~~ but ~~can~~ also be acquired in the ~~course~~ of ~~the project~~

* Some basic understanding of machine learning techniques, audio-fingerprinting, error correction or fuzzy cryptograpy might be helpful but will also be acquired throughout the project (Since there are implementations already, none of these have to be done from scratch)

* ~~Basic understanding of physical layer effects of wireless communication will~~ be helpful

* Experience with audio processing might be helpful but is not mandatory

@@ Line 13: / Line 13: @@
 In this implementation, similar recordings of ambient audio from mobile devices in proximity are utilised for the generation of a secure key.
 An attacker that is farther away is not able to guess the same key with a recording of her own since the features in the ambient audio differ with location.
+However, the robustness of the generated key is dependent on properties of ambient audio.
+In previous studies, we experienced best pairing characteristics when devices are in an environment with a single loud audio source.
+When environmental noise is, however, more ubiquitous and noisy, it is easier for an attacker to generate a key that is similar to that of the legitimate communication partners.
-[[File:AudioSynch.png]]
+In this project we will develop a mechanism to adapt the parameter of the underlying approach to generate secure to changing environmental parameters.
-Indoor positioning still remains a challenging task since GPS does not work indoors and reliable.
+[[File:AudioSynch.png]]
-Furthermore, ubiquitously deployed alternatives are not available.
-Recenty, localisation techniques based on radio frequency measurements have been proposed.
-In this project, the student(s) will design and implement an indoor localisation system based on RF-signal strength fluctuation.
-We will provide USRP software defined radio devices and further required hardware equipment to design an accurate RF-based indoor localisation system.
-In the frame of the 2014 IPSN Indoor Localisation Competition [http://ipsn.acm.org/2014/content/pdf/IndoorLocation-CFC.pdf] the designed system can be tested under realistic conditions.
-A participation is highly appreciated and will be supported.
 == Required Skills ==
 * Natural curiosity, high motivation and a good sense of creativity :)
-* Programming skills in Python will be required but can also be acquired in the course of the project
+* Some basic understanding of machine learning techniques, audio-fingerprinting, error correction or fuzzy cryptograpy might be helpful but will also be acquired throughout the project (Since there are implementations already, none of these have to be done from scratch)
-* Basic understanding of physical layer effects of wireless communication will be helpful
+* Experience with audio processing might be helpful but is not mandatory