This document summarises the results of several research campaigns over the past seven years. The main connecting theme is the physical layer of widely deployed sensors in Pervasive Computing domains. In particular, we have focused on the RF-channel or on ambient audio. The initial problem from which we started this work was that of distributed adaptive transmit beamforming. We have been looking for a simple method to align the phases of jointly transmitting nodes (e.g. sensor or IoT nodes). The algorithmic solution to this problem was to implement a distributed random optimisation method on the participating nodes in which the transmitters and the receiver follow an iterative question-and-answer scheme. We have been able to derive sharp asymptotic bounds on the expected optimisation time of an evolutionary random optimiser and presented an asymptotically optimal approach. One thing that we have learned from the work on these physical layer algorithms was that the signals we work on are fragile and perceptive to physical environmental changes. These could be obstacles such as furniture, opened or closed windows or doors as well as movement of individuals. This observation motivated us to view the wireless interface as a sensor for environmental changes in Pervasive Computing environments. Another use of physical layer RF-signals is for security applications. We are currently working to further push these mentioned directions and novel fields of physical prototyping. In particular, the calculation of mathematical operations on the wireless channel at the time of transmission appears to contain good potential for gains in efficiency for communication and computation in Pervasive Computing domains.
The last step is necessary since the similarity between fingerprints is typically not sufficient to establish a secure channel. With fuzzy-cryptography schemes, the generation of an identical key based on noisy input data @cite_66 is possible. analyse the usage of biometric or multimedia data as part of an authentication process and propose a protocol @cite_213 . Due to the use of error-tolerant cryptographic techniques, this protocol is robust against noise in the input data. The authors utilise a secure sketch @cite_130 to produce public information about an input without revealing it. The input can then be recovered given another value that is close to it. A similar study is presented by @cite_79 . The authors establish a key distribution based on a fuzzy vault @cite_179 using data measured by devices worn on the human body. The fuzzy vault scheme, also utilised in @cite_110 , enables the decryption of a secret with any key that is substantially similar to the key used for encryption.
@cite_130 @cite_79 @cite_179 @cite_110 @cite_213 @cite_66
2087355434 2146813141 2165367855 2026973959
We propose to establish a secure communication channel among devices based on similar audio patterns. Features from ambient audio are used to generate a shared cryptographic key between devices without exchanging information about the ambient audio itself or the features utilized for the key generation process. We explore a common audio-fingerprinting approach and account for the noise in the derived fingerprints by employing error correcting codes. This fuzzy-cryptography scheme enables the adaptation of a specific value for the tolerated noise among fingerprints based on environmental conditions by altering the parameters of the error correction and the length of the audio samples utilized. In this paper, we experimentally verify the feasibility of the protocol in four different realistic settings and a laboratory experiment. The case studies include an office setting, a scenario where an attacker is capable of reproducing parts of the audio context, a setting near a traffic loaded road, and a crowded canteen environment. We apply statistical tests to show that the entropy of fingerprints based on ambient audio is high. The proposed scheme constitutes a totally unobtrusive but cryptographically strong security mechanism based on contextual information. We provide formal definitions and efficient secure techniques for turning noisy information into keys usable for any cryptographic application, and, in particular, reliably and securely authenticating biometric data. Our techniques apply not just to biometric information, but to any keying material that, unlike traditional cryptographic keys, is (1) not reproducible precisely and (2) not distributed uniformly. We propose two primitives: a fuzzy extractor reliably extracts nearly uniform randomness @math from its input; the extraction is error-tolerant in the sense that @math will be the same even if the input changes, as long as it remains reasonably close to the original. Thus, @math can be used as a key in a cryptographic application. A secure sketch produces public information about its input @math that does not reveal @math and yet allows exact recovery of @math given another value that is close to @math . Thus, it can be used to reliably reproduce error-prone biometric inputs without incurring the security risk inherent in storing them. We define the primitives to be both formally secure and versatile, generalizing much prior work. In addition, we provide nearly optimal constructions of both primitives for various measures of “closeness” of input data, such as Hamming distance, edit distance, and set difference. In this paper, the fundamental insecurities hampering a scalable, wide-spread deployment of biometric authentication are examined, and a cryptosystem capable of using fingerprint data as its key is presented. For our application, we focus on situations where a private key stored on a smartcard is used for authentication in a networked environment, and we assume an attacker can launch o -line attacks against a stolen card.Juels and Sudan's fuzzy vault is used as a starting point for building and analyzing a secure authentication scheme using fingerprints and smartcards called a figerprint vault. Fingerprint minutiae coordinates mi are encoded as elements in a nite eld F and the secret key is encoded in a polynomial f(x) over F[x]. The polynomial is evaluated at the minutiae locations, and the pairs (mi, f(mi)) are stored along with random (ci, di) cha points such that di ≠ f(ci). Given a matching fingerprint, a valid user can seperate out enough true points from the cha points to reconstruct f(x), and hence the original secret key.The parameters of the vault are selected such that the attacker's vault unlocking complexity is maximized, subject to zero unlocking complexity with a matching fingerprint and a reasonable amount of error. For a feature location measurement variance of 9 pixels, the optimal vault is 269 times more difficult to unlock for an attacker compared to a user posessing a matching fingerprint, along with approximately a 30 chance of unlocking failure. Based on recent works showing the feasibility of key generation using biometrics, we study the application of handwritten signature to cryptography. Our signature-based key generation scheme implements the cryptographic construction named fuzzy vault. The use of distinctive signature features suited for the fuzzy vault is discussed and evaluated. Experimental results are reported, including error rates to unlock the secret data by using both random and skilled forgeries from the MCYT database.