Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Foundations of Stochastic Thermodynamics

Small systems in a thermodynamic medium --- like colloids in a suspension or the molecular machinery in living cells --- are strongly affected by the thermal fluctuations of their environment. Physicists model such systems by means of stochastic processes. Stochastic Thermodynamics (ST) defines entropy changes and other thermodynamic notions for individual realizations of such processes. It applies to situations far from equilibrium and provides a unified approach to stochastic fluctuation relations. Its predictions have been studied and verified experimentally. This thesis addresses the theoretical foundations of ST. Its focus is on the following two aspects: (i) The stochastic nature of mesoscopic observations has its origin in the molecular chaos on the microscopic level. Can one derive ST from an underlying reversible deterministic dynamics? Can we interpret ST's notions of entropy and entropy changes in a well-defined information-theoretical framework? (ii) Markovian jump processes on finite state spaces are common models for bio-chemical pathways. How does one quantify and calculate fluctuations of physical observables in such models? What role does the topology of the network of states play? How can we apply our abstract results to the design of models for molecular motors? The thesis concludes with an outlook on dissipation as information written to unobserved degrees of freedom --- a perspective that yields a consistency criterion between dynamical models formulated on various levels of description.Comment: Ph.D. Thesis, G\"ottingen 2014, Keywords: Stochastic Thermodynamics, Entropy, Dissipation, Markov processes, Large Deviation Theory, Molecular Motors, Kinesi

Preserving privacy in edge computing

Edge computing or fog computing enables realtime services to smart application users by storing data and services at the edge of the networks. Edge devices in the edge computing handle data storage and service provisioning. Therefore, edge computing has become a  new norm for several delay-sensitive smart applications such as automated vehicles, ambient-assisted living, emergency response services, precision agriculture, and smart electricity grids. Despite having great potential, privacy threats are the main barriers to the success of edge computing. Attackers can leak private or sensitive information of data owners and modify service-related data for hampering service provisioning in edge computing-based smart applications. This research takes privacy issues of heterogeneous smart application data into account that are stored in edge data centers. From there, this study focuses on the development of privacy-preserving models for user-generated smart application data in edge computing and edge service-related data, such as Quality-of-Service (QoS) data, for ensuring unbiased service provisioning. We begin with developing privacy-preserving techniques for user data generated by smart applications using steganography that is one of the data hiding techniques. In steganography, user sensitive information is hidden within nonsensitive information of data before outsourcing smart application data, and stego data are produced for storing in the edge data center. A steganography approach must be reversible or lossless to be useful in privacy-preserving techniques. In this research, we focus on numerical (sensor data) and textual (DNA sequence and text) data steganography. Existing steganography approaches for numerical data are irreversible. Hence, we introduce a lossless or reversible numerical data steganography approach using Error Correcting Codes (ECC). Modern lossless steganography approaches for text data steganography are mainly application-specific and lacks imperceptibility, and DNA steganography requires reference DNA sequence for the reconstruction of the original DNA sequence. Therefore, we present the first blind and lossless DNA sequence steganography approach based on the nucleotide substitution method in this study. In addition, a text steganography method is proposed that using invisible character and compression based encoding for ensuring reversibility and higher imperceptibility.  Different experiments are conducted to demonstrate the justification of our proposed methods in these studies. The searching capability of the stored stego data is challenged in the edge data center without disclosing sensitive information. We present a privacy-preserving search framework for stego data on the edge data center that includes two methods. In the first method, we present a keyword-based privacy-preserving search method that allows a user to send a search query as a hash string. However, this method does not support the range query. Therefore, we develop a range search method on stego data using an order-preserving encryption (OPE) scheme. In both cases, the search service provider retrieves corresponding stego data without revealing any sensitive information. Several experiments are conducted for evaluating the performance of the framework. Finally, we present a privacy-preserving service computation framework using Fully Homomorphic Encryption (FHE) based cryptosystem for ensuring the service provider's privacy during service selection and composition. Our contributions are two folds. First, we introduce a privacy-preserving service selection model based on encrypted Quality-of-Service (QoS) values of edge services for ensuring privacy. QoS values are encrypted using FHE. A distributed computation model for service selection using MapReduce is designed for improving efficiency. Second, we develop a composition model for edge services based on the functional relationship among edge services for optimizing the service selection process. Various experiments are performed in both centralized and distributed computing environments to evaluate the performance of the proposed framework using a synthetic QoS dataset

Molecular insights to crustacean phylogeny

This thesis aims to resolve internal relationships of the major crustacean groups inferring phylogenies with molecular data. New molecular and neuroanatomical data support the scenario that the Hexapoda might have evolved from Crustacea. Most molecular studies of crustaceans relied on single gene or multigene analyses in which for most cases partly sequenced rRNA genes were used. However, intensive data quality and alignment assessments prior to phylogenetic reconstructions are not conducted in most studies. One methodological aim in this thesis was to implement new tools to infer data quality, to improve alignment quality and to test the impact of complex modeling of the data. Two of the three phylogenetic analyses in this thesis are also based on rRNA genes. In analysis (A) 16S rRNA, 18S rRNA and COI sequences were analyzed. RY coding of the COI fragment, an alignment procedure that considers the secondary structure of RNA molecules and the exclusion of alignment positions of ambiguous positional homology was performed to improve data quality. Anyhow, by extensive network reconstructions it was shown that the signal quality in the chosen and commonly used markers is not suitable to infer crustacean phylogeny, despite the extensive data processing and optimization. This result draws a new light on previous studies relying on these markers. In analyses (B) completely sequenced 18S and 28S rRNA genes were used to reconstruct the phylogeny. Base compositional heterogeneity was taken into account based on the finding of analysis (A), additionally to secondary structure alignment optimization and alignment assessment. The complex modeling to compare time-heterogeneous versus time-homogenous processes in combination with mixed models for an implementation of secondary structures was only possible applying the Bayesian software package PHASE. The results clearly demonstrated that complex modeling counts and that ignoring time-heterogeneous processes can mislead phylogenetic reconstructions. Some results enlight the phylogeny of Crustaceans, for the first time the Cephalocarida (Hutchinsoniella macracantha) were placed in a clade with the Branchiopoda, which morphologically is plausible. Compared to the time-homogeneous tree the time-heterogeneous tree gives lower support values for some nodes. It can be suggested, that the incorporation of base compositional heterogeneity in phylogenetic analysis improves the reliability of the topology. The Pancrustacea are supported maximally in both approaches, but internal relations are not reliably reconstructed. One result of this analysis is that the phylogenetic signal in rRNA data might be eroded for crustaceans. Recent publications presented analyses based on phylogenomic data, to reconstruct mainly metazoan phylogeny. The supermatrix method seems to outperform the supertree approach. In this analysis the supermatrix approach was applied. Crustaceans were collected to conduct EST sequencing projects and to include the resulting sequences combined with public sequence data into a phylogenomic analysis (C). New and innovative reduction heuristics were performed to condense the dataset. The results showed that the matrix implementation of the reduced dataset ends in a more reliable topology in which most node values are highly supported. In analysis (C) the Branchiopoda were positioned as sister-group to Hexapoda, a differing result to analysis (A) and (B), but that is in line with other phylogenomic studies

Reducing Internet Latency : A Survey of Techniques and their Merit

Bob Briscoe, Anna Brunstrom, Andreas Petlund, David Hayes, David Ros, Ing-Jyh Tsang, Stein Gjessing, Gorry Fairhurst, Carsten Griwodz, Michael WelzlPeer reviewedPreprin

An Energy-Efficient and Reliable Data Transmission Scheme for Transmitter-based Energy Harvesting Networks

Energy harvesting technology has been studied to overcome a limited power resource problem for a sensor network. This paper proposes a new data transmission period control and reliable data transmission algorithm for energy harvesting based sensor networks. Although previous studies proposed a communication protocol for energy harvesting based sensor networks, it still needs additional discussion. Proposed algorithm control a data transmission period and the number of data transmission dynamically based on environment information. Through this, energy consumption is reduced and transmission reliability is improved. The simulation result shows that the proposed algorithm is more efficient when compared with previous energy harvesting based communication standard, Enocean in terms of transmission success rate and residual energy.This research was supported by Basic Science Research Program through the National Research Foundation by Korea (NRF) funded by the Ministry of Education, Science and Technology(2012R1A1A3012227)