Practical Lattice-Based Digital Signature Schemes

Digital signatures are an important primitive for building secure systems and are used in most real-world security protocols. However, almost all popular signature schemes are either based on the factoring assumption (RSA) or the hardness of the discrete logarithm problem (DSA/ECDSA). In the case of classical cryptanalytic advances or progress on the development of quantum computers, the hardness of these closely related problems might be seriously weakened. A potential alternative approach is the construction of signature schemes based on the hardness of certain lattice problems that are assumed to be intractable by quantum computers. Due to significant research advancements in recent years, lattice-based schemes have now become practical and appear to be a very viable alternative to number-theoretic cryptography. In this article, we focus on recent developments and the current state of the art in lattice-based digital signatures and provide a comprehensive survey discussing signature schemes with respect to practicality. Additionally, we discuss future research areas that are essential for the continued development of lattice-based cryptography.

Design and Development of Spectral Signature Based Chipless RFID Tags

The main objective of this thesis is to design and develop spectral signature based chipless RFID tags Multiresonators are essential component of spectral signature based chipless tags. To enhance the data coding capacity in spectral signature based tags require large number of resonances in a limited bandwidth. The frequency of the resonators have to be close to each other. To achieve this condition, the quality factor of each resonance needs to be high. The thesis discusses about various types of multiresonators, their practical implementation and how they can be used in design. Encoding of data into spectral domain is another challenge in chipless tag design. Here, the technique used is the presence or absence encoding technique. The presence of a resonance is used to encode Logic 1 and absence of a speci c resonance is used to encode Logic 0. Di erent types of multiresonators such as open stub multiresonators, coupled bunch hairpin resonators and shorted slot ground ring resonator are proposed in this thesis.

Multi-attribute collaborative filtering recommendation based on improved group decision-making

Currently researchers in the field of personalized recommendations bear little consideration on users' interest differences in resource attributes although resource attribute is usually one of the most important factors in determining user preferences. To solve this problem, the paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified Pearson-Compatibility multi-attribute group decision-making algorithm, and introduces an algorithm to solve the recommendation problem of k-neighbor similar users. Considering the characteristics of collaborative filtering recommendation, the paper addresses the issues on the preference differences of similar users, incomplete values, and advanced converge of the algorithm. Thus the paper realizes multi-attribute collaborative filtering. Finally, the effectiveness of the algorithm is proved by an experiment of collaborative recommendation among multi-users based on virtual environment. The experimental results show that the algorithm has a high accuracy on predicting target users' attribute preferences and has a strong anti-interference ability on deviation and incomplete values.

Evolutionary placement of Xanthomonadales based on conserved protein signature sequences

Xanthomonadales comprises one of the largest phytopathogenic bacterial groups, and is currently classified within the gamma-proteobacteria. However, the phylogenetic placement of this group is not clearly resolved, and the results of different studies contradict one another. In this work, the evolutionary position of Xanthomonadales was determined by analyzing the presence of shared insertions and deletions (INDELs) in highly conserved proteins. Several distinctive insertions found in most of the members of the gamma-proteobacteria are absent in Xanthomonadales and groups such as Legionelalles, Chromatiales, Methylococcales, Thiotrichales and Cardiobacteriales. These INDELs were most likely introduced after the branching of Xanthomonadales from most of the gamma-proteobacteria and provide evidence for the phylogenetic placement of the early gamma-proteobacteria. Moreover, other proteins contain insertions exclusive to the Xanthomonadales order, confirming that this is a monophyletic group and provide important specific genetic markers. Thus, the data presented clearly support the Xanthomonadales group as an independent subdivision, and constitute one of the deepest branching lineage within the gamma-proteobacteria clade. (C) 2009 Elsevier Inc. All rights reserved.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.

Towards designing privacy-preserving signature-based IDS as a service : a study and practice

With the advent of Cloud Computing, IDS as a service (IDSaaS) has been proposed as an alternative to protect a network (e.g., financial organization) from a wide range of network attacks by offloading the expensive operations such as the process of signature matching to the cloud. The IDSaaS can be roughly classified into two types: signature-based detection and anomaly-based detection. During the packet inspection, no party wants to disclose their own data especially sensitive information to others, even to the cloud provider, for privacy concerns. However, current solutions of IDSaaS have not much discussed this issue. In this work, focus on the signature-based IDSaaS, we begin by designing a promising privacy-preserving intrusion detection mechanism, the main feature of which is that the process of signature matching does not reveal any specific content of network packets by means of a fingerprint-based comparison. We further conduct a study to evaluate this mechanism under a cloud scenario and identify several open problems and issues for designing such a privacy-preserving mechanism for IDSaaS in a practical environment.

Short designated verifier signature scheme and its identity-based variant

The notion of strong designated verifier signature was put forth by Jakobsson, Sako and Impagliazzo in 1996, but the formal definition was defined recently by Saeednia, Kremer and Markowitch in 2003 and revisited by Laguil- laumie and Vergnaud in 2004. In this paper, we firstly propose the notion of short strong designated verifier sig- nature scheme, and extend it to the short identity-based strong designated verifier scheme. Then, we propose the first construction of short strong designated verifier sig- nature scheme. We also extend our scheme to construct a short identity-based strong designated verifier signature scheme. The size of the signature of our schemes is the shortest compared to any existing schemes reported in the literature. We provide formal security proofs for our schemes based on the random oracle model. Finally, we also discuss an extension of our scheme to construct a short strong designated verifier signature without random oracle.

IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples

Background: Novel predictive markers are needed to accurately diagnose the breast cancer patients so they do not need to undergo any unnecessary aggressive therapies. Various gene expression studies based predictive gene signatureshave generated in the recent past to predict the binary estrogen-receptor subclass or to predict the therapy response subclass. However, the existing algorithms comes with many limitations, including low predictive performances over multiple cohorts of patients and non-significant or limited biological roles associated with thepredictive gene signatures. Therefore, the aim of this study is to develop novel predictive markers with improved performances.Methods: We propose a novel prediction algorithm called IPA to construct a predictive gene signature for performing multiple prediction tasks of predicting estrogen-receptor based binary subclass and predicting chemotherapy response (neoadjuvantly) based binary subclass. The constructed gene signature with considering multiple classification techniques was used to evaluate the algorithm performance on multiple cohorts of breast cancer patients.Results: The evaluation on multiple validation cohorts demonstrated that proposed algorithm achieved stable and high performance to perform prediction tasks, with consideration given to any classification techniques. We show that the predictive gene signature of our proposed algorithm reflects the mechanisms underlying the estrogen-receptors or response to therapy with significant greater biological interpretations, compared with the other existing algorithm.

Optimisation of solid-phase microextraction combined with gas chromatography–mass spectrometry based methodology to establish the global volatile signature in pulp and skin of Vitis vinifera L. grape varieties

The volatiles (VOCs) and semi-volatile organic compounds (SVOCs) responsible for aroma are mainly present in skin of grape varieties. Thus, the present investigation is directed towards the optimisation of a solvent free methodology based on headspace-solid-phase microextraction (HS-SPME) combined with gas chromatography–quadrupole mass spectrometry (GC–qMS) in order to establish the global volatile composition in pulp and skin of Bual and Bastardo Vitis vinifera L. varieties. A deep study on the extraction-influencing parameters was performed, and the best results, expressed as GC peak area, number of identified compounds and reproducibility, were obtained using 4 g of sample homogenised in 5 mL of ultra-pure Milli-Q water in a 20 mL glass vial with addition of 2 g of sodium chloride (NaCl). A divinylbenzene/carboxen/polydimethylsiloxane fibre was selected for extraction at 60 °C for 45 min under continuous stirring at 800 rpm. More than 100 VOCs and SVOCs, including 27 monoterpenoids, 27 sesquiterpenoids, 21 carbonyl compounds, 17 alcohols (from which 2 aromatics), 10 C13 norisoprenoids and 5 acids were identified. The results showed that, for both grape varieties, the levels and number of volatiles in skin were considerably higher than those observed in pulp. According to the data obtained by principal component analysis (PCA), the establishment of the global volatile signature of grape and the relationship between different part of grapes—pulp and skin, may be an useful tool to winemaker decision to define the vinification procedures that improves the organoleptic characteristics of the corresponding wines and consequently contributed to an economic valorization and consumer acceptance.

Approccio network-based alla discriminant analysis mediante HPC per la ricerca di signature ottimali in dati ad alta dimensionalità

Il presente lavoro di tesi si inserisce nell’ambito della classificazione di dati ad alta dimensionalità, sviluppando un algoritmo basato sul metodo della Discriminant Analysis. Esso classifica i campioni attraverso le variabili prese a coppie formando un network a partire da quelle che hanno una performance sufficientemente elevata. Successivamente, l’algoritmo si avvale di proprietà topologiche dei network (in particolare la ricerca di subnetwork e misure di centralità di singoli nodi) per ottenere varie signature (sottoinsiemi delle variabili iniziali) con performance ottimali di classificazione e caratterizzate da una bassa dimensionalità (dell’ordine di 101, inferiore di almeno un fattore 103 rispetto alle variabili di partenza nei problemi trattati). Per fare ciò, l’algoritmo comprende una parte di definizione del network e un’altra di selezione e riduzione della signature, calcolando ad ogni passaggio la nuova capacità di classificazione operando test di cross-validazione (k-fold o leave- one-out). Considerato l’alto numero di variabili coinvolte nei problemi trattati – dell’ordine di 104 – l’algoritmo è stato necessariamente implementato su High-Performance Computer, con lo sviluppo in parallelo delle parti più onerose del codice C++, nella fattispecie il calcolo vero e proprio del di- scriminante e il sorting finale dei risultati. L’applicazione qui studiata è a dati high-throughput in ambito genetico, riguardanti l’espressione genica a livello cellulare, settore in cui i database frequentemente sono costituiti da un numero elevato di variabili (104 −105) a fronte di un basso numero di campioni (101 −102). In campo medico-clinico, la determinazione di signature a bassa dimensionalità per la discriminazione e classificazione di campioni (e.g. sano/malato, responder/not-responder, ecc.) è un problema di fondamentale importanza, ad esempio per la messa a punto di strategie terapeutiche personalizzate per specifici sottogruppi di pazienti attraverso la realizzazione di kit diagnostici per l’analisi di profili di espressione applicabili su larga scala. L’analisi effettuata in questa tesi su vari tipi di dati reali mostra che il metodo proposto, anche in confronto ad altri metodi esistenti basati o me- no sull’approccio a network, fornisce performance ottime, tenendo conto del fatto che il metodo produce signature con elevate performance di classifica- zione e contemporaneamente mantenendo molto ridotto il numero di variabili utilizzate per questo scopo.

A proteome signature for intrauterine growth restriction derived from multifactorial analysis of mass spectrometry-based cord blood serum profiling

Intrauterine growth restriction (IUGR) is defined as a condition in which the fetus does not reach its genetically given growth potential, resulting in low birth weight. IUGR is an important cause of perinatal morbidity and mortality, thus contributing substantially to medically indicated preterm birth in order to prevent fetal death. We subjected umbilical cord blood serum samples either belonging to the IUGR group (n = 15) or to the control group (n = 15) to fractionation by affinity chromatography using a bead system with hydrophobic interaction capabilities. So prepared protein mixtures were analyzed by MALDI-TOF mass spectrometric profiling. The six best differentiating ion signals at m/z 8205, m/z 8766, m/z 13 945, m/z 15 129, m/z 15 308, and m/z 16 001 were collectively assigned as IUGR proteome signature. Separation confidence of our IUGR proteome signature reached a sensitivity of 0.87 and a specificity of 0.93. Assignment of ion signals in the mass spectra to specific proteins was substantiated by SDS-PAGE in conjunction with peptide mass fingerprint analysis of cord blood serum proteins. One constituent of this proteome signature, apolipoprotein C-III(0) , a derivative lacking glycosylation, has been found more abundant in the IUGR cord blood serum samples, irrespective of gestational age. Hence, we suggest apolipoprotein C-III(0) as potential key-marker of the here proposed IUGR proteome signature, as it is a very low-density lipoprotein (VLDL) and high-density lipoprotein (HDL) member and as such involved in triglyceride metabolism that itself is discussed as being of importance in IUGR pathogenesis. Our results indicate that subtle alterations in protein glycosylation need to be considered for improving our understanding of the pathomechanisms in IUGR.

Search for direct chargino production in anomaly-mediated supersymmetry breaking models based on a disappearing-track signature in pp collisions at sqrts=7 TeV with the ATLAS detector

A search for direct chargino production in anomaly-mediated supersymmetry breaking scenarios is performed in p p collisions at root s = 7 TeV using 4.7 fb(-1) of data collected with the ATLAS experiment at the LHC. In these models, the lightest chargino is predicted to have a lifetime long enough to be detected in the tracking detectors of collider experiments. This analysis explores such models by searching for chargino decays that result in tracks with few associated hits in the outer region of the tracking system. The transverse-momentum spectrum of candidate tracks is found to be consistent with the expectation from the Standard Model background processes and constraints on chargino properties are obtained.