A Topological Study of Chaotic Iterations. Application to Hash Functions

International audienceChaotic iterations, a tool formerly used in distributed computing, has recently revealed various interesting properties of disorder leading to its use in the computer science security field. In this paper, a comprehensive study of its topological behavior is proposed. It is stated that, in addition to being chaotic as defined in the Devaney's formulation, this tool possesses the property of topological mixing. Additionally, its level of sensibility, expansivity, and topological entropy are evaluated. All of these properties lead to a complete unpredictable behavior for the chaotic iterations. As it only manipulates binary digits or integers, we show that it is possible to use it to produce truly chaotic computer programs. As an application example, a truly chaotic hash function is proposed in two versions. In the second version, an artificial neural network is used, which can be stated as chaotic according to Devaney

Secure Data Aggregation in Wireless Sensor Networks. Homomorphism versus Watermarking Approach

International audienceWireless sensor networks are now in widespread use to monitor regions, detect events and acquire information. Since the deployed nodes are separated, they need to cooperatively communicate sensed data to the base station. Hence, transmissions are a very energy consuming operation. To reduce the amount of sending data, an aggregation approach can be applied along the path from sensors to the sink. However, usually the carried information contains confidential data. Therefore, an end-to-end secure aggregation approach is required to ensure a healthy data reception. End-to-end encryption schemes that support operations over cypher-text have been proved important for private party sensor network implementations. These schemes offer two main advantages: end-to-end concealment of data and ability to operate on cipher text, then no more decryption is required for aggregation. Unfortunately, nowadays these methods are very complex and not suitable for sensor nodes having limited resources. In this paper, we propose a secure end-to-end encrypted-data aggregation scheme. It is based on elliptic curve cryptography that exploits a smaller key size. Additionally, it allows the use of higher number of operations on cypher-texts and prevents the distinction between two identical texts from their cryptograms. These properties permit to our approach to achieve higher security levels than existing cryptosystems in sensor networks. Our experiments show that our proposed secure aggregation method significantly reduces computation and communication overhead and can be practically implemented in on-the-shelf sensor platforms. By using homomorphic encryption on elliptic curves, we thus have realized an efficient and secure data aggregation in sensor networks. Lastly, to enlarge the aggregation functions that can be used in a secure wireless sensor network, a watermarking-based authentication scheme is finally proposed

Mutations in TUBG1, DYNC1H1, KIF5C and KIF2A cause malformations of cortical development and microcephaly

The genetic causes of malformations of cortical development (MCD) remain largely unknown. Here we report the discovery of multiple pathogenic missense mutations in TUBG1, DYNC1H1 and KIF2A, as well as a single germline mosaic mutation in KIF5C, in subjects with MCD. We found a frequent recurrence of mutations in DYNC1H1, implying that this gene is a major locus for unexplained MCD. We further show that the mutations in KIF5C, KIF2A and DYNC1H1 affect ATP hydrolysis, productive protein folding and microtubule binding, respectively. In addition, we show that suppression of mouse Tubg1 expression in vivo interferes with proper neuronal migration, whereas expression of altered gamma-tubulin proteins in Saccharomyces cerevisiae disrupts normal microtubule behavior. Our data reinforce the importance of centrosomal and microtubule-related proteins in cortical development and strongly suggest that microtubule-dependent mitotic and postmitotic processes are major contributors to the pathogenesis of MCD

Large datasets: a mixed method to adapt and improve their learning by neural networks used in regression contexts

Part 9: Machine LearningInternational audienceThe purpose of this work is to further study the relevance of accelerating the Monte-Carlo calculations for the gamma rays external radiotherapy through feed-forward neural networks. We have previously presented a parallel incremental algorithm that builds neural networks of reduced size, while providing high quality approximations of the dose deposit~\cite{Vecpar08b}. Our parallel algorithm consists in an optimized decomposition of the initial learning dataset (also called learning domain) in as much subsets as available processors. However, although that decomposition provides subsets of similar signal complexities, their sizes may be quite different, still implying potential differences in their learning times. This paper presents an efficient data extraction allowing a good and balanced training without any loss of signal information. As will be shown, the resulting irregular decomposition permits an important improvement in the learning time of the global network

Stability of fully asynchronous discrete-time discrete-state dynamic networks

International audienc