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.
@cite_130: We provide formal definitions and efficient secure techniques for turning biometric information into keys usable for any cryptographic application, and reliably and securely authenticating biometric data.
@cite_79: The security of wireless body sensor network (BSN) is very important to telemedicine and m-healthcare, and it still remains a critical challenge. This paper presents a novel key distribution solution which allows two sensors in one BSN to agree on a changeable cryptographic key. A previously published scheme, fuzzy vault, is firstly applied to secure the random cryptographic key generated from electrocardiographic (ECG) signals. Simulations based on ECG data from MIT PhysioBank database, produce a minimum half total error rate (HTER) of 0.65 , which demonstrates our key distribution solution is promising compared with previous method, with HTER of 4.26 .
@cite_179: We describe a simple and novel cryptographic construction that we call a fuzzy vault. Alice may place a secret value spl kappa in a fuzzy vault and "lock" it using an unordered set A of elements from some public universe U. If Bob tries to "unlock" the vault using an unordered set B, he obtains spl kappa only if B is close to A, i.e., only if A and B overlap substantially.
@cite_110: Consider two parties holding samples from correlated distributions @math and @math , respectively, where these samples are within distance @math of each other in some metric space. The parties wish to agree on a close-to-uniformly distributed secret key @math by sending a single message over an insecure channel controlled by an all-powerful adversary who may read and modify anything sent over the channel. We consider both the keyless case, where the parties share no additional secret information, and the keyed case, where the parties share a long-term secret @math that they can use to generate a sequence of session keys @math using multiple pairs @math . The former has applications to, e.g., biometric authentication, while the latter arises in, e.g., the bounded-storage model with errors. We show solutions that improve upon previous work in several respects. The best prior solution for the keyless case with no errors (i.e., @math ) requires the min-entropy of @math to exceed @math , where @math is the bit length of @math . Our solution applies whenever the min-entropy of @math exceeds the minimal threshold @math , and yields a longer key.
@cite_213: In order to apply cryptographic operations on noisy data, a recent approach employs some additional public data, known as secure sketch, to correct the noise so that consistent outcome can be obtained. This approach can be employed to extract authentication tags from noisy multimedia or biometric objects, by including the sketch in the tags. However, there are a few issues that need to be addressed. Firstly, those objects are typically represented in a continuous domain, and hence further quantization is required in order to obtain a short authentication tag. Secondly, for the purpose of authentication, forgery and preimage attacks are major concerns. However, such attacks are not considered in the notion of secure sketch. To handle the first issue, we give a construction using two levels of quantization. The second issue leads to the proposed additional requirement on sensitivity. We study how to choose the optimal parameters under the trade-off of robustness, size and sensitivity, and show that in many practical settings, the two-level quantization can be significantly more effective than a seemingly natural method of assigning one bit to each coefficient.
@cite_66: An overview was given of security applications where noisy data plays a substantial role. Secure Sketches and Fuzzy Extractors were discussed at tutorial level, and two simple Fuzzy Extractor constructions were shown. One of the latest developments was presented: quantum-readout PUFs.
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.