Η παρούσα διατριβή ασχολείται με τη μελέτη, την ανάπτυξη και τη θεμελίωση νέων μεθόδων Υπολογιστικής Νοημοσύνης και Ευφυούς Βελτιστοποίησης. Συνοπτικά οργανώνεται στα ακόλουθα τρία μέρη: Αρχικά παρουσιάζεται το πεδίο της Υπολογιστικής Νοημοσύνης και πραγματοποιείται μία σύντομη αναφορά στους τρεις κύριους κλάδους της, τον Εξελικτικό Υπολογισμό, τα Τεχνητά Νευρωνικά Δίκτυα και τα Ασαφή Συστήματα. Το επόμενο μέρος αφιερώνεται στην παρουσίαση νέων, καινοτόμων οικογενειών των αλγορίθμων Βελτιστοποίησης Σμήνους Σωματιδίων (ΒΣΣ) και των Διαφοροεξελικτικών Αλγόριθμων (ΔΕΑ), για την επίλυση αριθμητικών προβλημάτων βελτιστοποίησης χωρίς περιορισμούς, έχοντας είτε ένα, είτε πολλαπλούς ολικούς βελτιστοποιητές. Οι αλγόριθμοι ΒΣΣ και ΔΕΑ αποτελούν τις βασικές μεθοδολογίες της παρούσας διατριβής. Όλες οι οικογένειες μεθόδων που προτείνονται, βασίζονται σε παρατηρήσεις των κοινών δομικών χαρακτηριστικών των ΒΣΣ και ΔΕΑ, ενώ η κάθε προτεινόμενη οικογένεια τις αξιοποιεί με διαφορετικό τρόπο, δημιουργώντας νέες, αποδοτικές μεθόδους με αρκετά ενδιαφέρουσες ιδιότητες και δυναμική. Η παρουσίαση του ερευνητικού έργου της διατριβής ολοκληρώνεται με το τρίτο μέρος στο οποίο περιλαμβάνεται μελέτη και ανάπτυξη μεθόδων ολικής βελτιστοποίησης για την εκπαίδευση Τεχνητών Νευρωνικών Δικτύων Υψηλής Τάξης, σε σειριακά και παράλληλα ή / και κατανεμημένα υπολογιστικά συστήματα. Η διδακτορική διατριβή ολοκληρώνεται με βασικά συμπεράσματα και τη συνεισφορά της. / The main subject of the thesis at hand revolves mainly around the development and foundations of new methods of computational intelligence and intelligent optimization. The thesis is organized into the following three parts: Firstly, we briefly present an overview of the field of Computational Intelligence, by describing its main categories, the Evolutionary Computation, the Artificial Neural Networks and the Fuzzy Systems. In the second part, we provide a detailed description of the newly developed families of algorithms for solving unconstrained numerical optimization problems in continues spaces with at least one global optimum. The proposed families are based on two well-known and widely used algorithms, namely the Particle Swarm Optimization (PSO) and the Differential Evolution (DE) algorithm. Both DE and PSO are the basic components for almost all methodologies proposed in the thesis. The proposed methodologies are based on common observations of the dynamics, the structural and the spacial characteristics of DE and PSO algorithms. Four novel families are presented in this part which exploit the aforementioned characteristics of the DE and the PSO algorithms. The proposed methodologies are efficient methods with quite interesting properties and dynamics. The presentation and description of our research contribution ends with the third and last part of the thesis, which includes the study and the development of novel global optimization methodologies for training Higher order Artificial Neural Networks in serial and parallel / distributed computational environments. The thesis ends with a brief summary, conclusions and discussion of the contribution of this thesis.

This dissertation investigates and introduces novel algorithms, theories, and supporting frameworks to significantly improve the growing problem of Internet security. A distributed firewall and active response architecture is introduced that enables any device within a cyber environment to participate in the active discovery and response of cyber attacks. A theory of semantic association systems is developed for the general problem of knowledge discovery in data. The theory of semantic association systems forms the basis of a novel semantic path merger packet classification algorithm. The theoretical aspects of the semantic path merger packet classification algorithm are investigated, and the algorithm's hardware-based implementation is evaluated along with comparative analysis versus content addressable memory. Experimental results show that the hardware implementation of the semantic path merger algorithm significantly outperforms content addressable memory in terms of energy consumption and operational timing.

Η βιομηχανία παραγωγής ρούχων, ακόμα και σήμερα, αποτελεί ένα βιομηχανικό κλάδο όπου η αυτοματοποίηση είναι εν μέρει σχεδόν ανύπαρκτη και εν γένει σε εμβρυικό στάδιο. Ένα μεγάλο τμήμα της γραμμής παραγωγής αποτελεί το τμήμα κατά το οποίο κομμάτια υφάσματος ράβονται σε μία ραπτομηχανή. Το στάδιο της ραφής, παρόλο που είναι το μεγαλύτερο σε χρονική διάρκεια και το σημαντικότερο στην τελική ποιότητα, παραμένει σχεδόν εξολοκλήρου μία χειρονακτική εργασία. Οι ιδιαιτερότητες των υφασμάτων όπως: η πολύ μικρή αντίστασή τους σε κάμψη, οι μεγάλες παραμορφώσεις τους, η απρόβλεπτη στατική/δυναμική συμπεριφορά τους, η ανισότροπη και μη-γραμμική φύση τους και η ανομοιογένειά τους είναι οι κύριοι παράγοντες των έντονων δυσκολιών κατά τον χειρισμό τους. Η ρομποτική ραφή είναι ένα σχετικά νέο ερευνητικό πεδίο αλλά και εξαιρετικά πολύπλοκο πρόβλημα, όπου ελάχιστοι ερευνητές έχουν προσπαθήσει να προσεγγίσουν. Αυτή τη στιγμή, από όσο είναι δυνατόν να είναι γνωστόν, μόνο τρία ερευνητικά κέντρα ασχολούνται, σε διεθνή επίπεδο, ενώ το ένα από αυτά είναι η Ομάδα Ρομποτικής του Πανεπιστημίου Πατρών. Στόχος της διατριβής είναι η ανάπτυξη ενός ευέλικτου συστήματος το οποίο ενσωματώνει όλα εκείνα τα ευφυή χαρακτηριστικά ώστε να συμβάλει στη ρομποτική αυτοματοποίηση της ραφής υφασμάτων. Η επίτευξη αυτού του στόχου στηρίζεται σε μεθόδους υπολογιστικής νοημοσύνης, σε τακτικές εμπνευσμένες από τον τρόπο εργασίας του ίδιου του ανθρώπου και στην ποιοτική διαχείριση της γνώσης/δεδομένων που εμπεριέχουν ασάφεια. Ταυτόχρονα, επιτυγχάνεται ο έλεγχος της ραφής χωρίς τη χρήση αναλυτικών μοντέλων τόσο των υφασμάτων όσο και της διαδικασίας. Ο συντονισμός όλων των προηγουμένων επιτυγχάνεται από ένα ευέλικτο ευφυές ιεραρχικό σύστημα ελέγχου της διαδικασίας της ραφής. Στην παρούσα διατριβή αναπτύχθηκε ένα σύνολο νέων μεθόδων για την αυτοματοποιημένη ραφή υφασμάτων με ένα βιομηχανικό ρομπότ εφοδιασμένο με έναν αισθητήρα δύναμης και μία κλασσικού τύπου ραπτομηχανή. Η πρωτότυπη προσέγγιση που αποτελεί το πλαίσιο της διατριβής αποτελείται από ένα ιεραρχικό σύστημα εκτιμήσεων, αποφάσεων και ελέγχου της διαδικασίας ραφής. Γίνεται μία συστηματική μελέτη, καταγραφή και αξιολόγηση όλων των χειρισμών του υφάσματος, πριν και κατά τη διάρκεια της ραφής, ως προς τις απαιτήσεις τους σε συστήματα αισθητήρων αλλά και στρατηγικών ελέγχου. Αναπτύσσεται μία πρωτότυπη βάση δεδομένων και ένα έμπειρο σύστημα που ενσωματώνουν όλη αυτή την προγενέστερη γνώση και εμπειρία. Παρουσιάζεται η ιδέα της εφαρμογής μεθόδων υπολογιστικής νοημοσύνης στο επιστημονικό πεδίο της ραφής υφασμάτων και εφαρμογής μεθόδων ευφυούς ελέγχου, για την τάνυση των υφασμάτων, βασισμένες σε νευρωνικά δίκτυα οι οποίες, για πρώτη φορά, χρησιμοποιούν ποιοτικές εκτιμήσεις των ιδιοτήτων και των χαρακτηριστικών των υφασμάτων. Έτσι, εισάγεται η έννοια των λεκτικών μεταβλητών για την ποιοτική περιγραφή των ιδιοτήτων των υφασμάτων. Στο πλαίσιο αυτό, εκφράζεται για πρώτη φορά, ποιοτικά σαν ποσοστό, η έννοια της “εκτατότητας” (extensibility) των υφασμάτων, που είναι μία βασική και καθοριστική ιδιότητά τους κατά την εργασία της ραφής τους. Επίσης περιγράφεται η ανάπτυξη μιας νέας μεθόδου αυτοματοποιημένου πειραματικού προσδιορισμού της “εκτατότητας” των υφασμάτων. Η πρωτότυπη αυτή προσέγγιση εφαρμόστηκε σε μονά και διπλά υφάσματα ενώ τα πειράματα εφελκυσμού ενσωματώθηκαν “έξυπνα” στην ίδια τη ραπτομηχανή και λίγο πριν τη διαδικασία της ραφής. Παρουσιάζεται ένα ασαφές σύστημα εξαγωγής της επιθυμητής τάνυσης, κατά τη ραφή του κάθε υφάσματος, το οποίο βασίζεται στην ποιοτική εκτίμηση της “εκτατότητας” του υφάσματος και διαμορφώθηκε από την εμπειρία του έμπειρου χειριστή και την ασαφή συνεπαγωγή που κάνει αυτός ανάμεσα στην “εκτατότητα” και την επιθυμητή τάνυση. Αναπτύχθηκε μία νέα μέθοδος ελέγχου της τάνυσης του υφάσματος κατά τη ραφή του. Σε αυτή χρησιμοποιήθηκε ένας νευρωνικός ελεγκτής ευθείας τροφοδότησης για τον έλεγχο του ρομποτικού άκρου εργασίας με σκοπό τη διατήρηση της τάνυσης του υφάσματος σύμφωνα με την εκάστοτε επιθυμητή. Επιπροσθέτως, παρουσιάζεται η επέκταση της προτεινόμενης προσέγγισης, του νευρωνικού ελέγχου της τάνυσης, η οποία έδειξε τη δυνατότητα γενίκευσής της και σε άλλες παραπλήσιες εργασίες. Επίσης, καταγράφονται τα νέα ερευνητικά πεδία που ανοίγουν στο χώρο της συνεργασίας ανθρώπου-ρομπότ για τον χειρισμό υπερ-εύκαμπτων και εύκαμπτων αντικειμένων. Τελικά, παρουσιάζεται μία συστηματική εργαστηριακή εκτέλεση ραφών, σε μία μεγάλη ποικιλία τόσο μονών όσο και διπλών υφασμάτων, με ρομπότ ενώ συγκρίθηκαν αυτές με ραφές από έμπειρους. Μέσα από τις πειραματικές ραφές αναδεικνύεται, η αναζητούμενη ευελιξία του συστήματος και η ικανοποιητική απόκρισή του σε μία μεγάλη ποικιλομορφία υφασμάτων. / The clothing industry, even today, is an industry where automation is partly almost nonexistent and generally in their infancy stages. A large part of the production line is the part in which pieces of fabrics are sewn using a sewing machine. The sewing process, while being the largest in duration and the most important to the final quality of the cloths, remains almost entirely one handiwork. The specifics of fabrics like: their very little bending resistance, their large deformations, their unpredictable static / dynamic behavior, their anisotropic and non-linear nature and heterogeneity are the main factors of acute difficulties in their handling. The robotized sewing of fabrics is a relatively new research field but it is also an extremely complex problem, where few researchers have tried to investigate it. Right now, as far as can be known, only three research centers in international level are involved this field, while one of them is the Robotics Group of the University of Patras. The aim of this thesis is to develop a flexible system which incorporates all those intelligent features in order to help automate robotic sewing of fabrics. This objective is based on computational intelligence methods, in approaches inspired by the way a human works and finally in the qualitative management of the knowledge/data that involve uncertainties. Simultaneously, the sewing control is achieved without using analytical models of both the fabrics and process. The coordination, of all of the previous mentioned, is achieved by a flexible hierarchical intelligent control system of the sewing process. This thesis developed a set of new set of methods for automated sewing of fabrics using an industrial robot, equipped with a force sensor, and a conventional sewing machine. The novel approach, which composes the main framework of this thesis, consists of a hierarchical system of estimations, decisions and process control of the sewing task. It is a systematic study, of recording and evaluating all the fabric handling tasks before and during the sewing process, according to the necessary requirements in sensor systems and control strategies. An original database and an expert system incorporating all this prior knowledge and experience is developed. The idea of applying methods of computational intelligence to the scientific field of sewing fabrics is presented. Also, the application of intelligent control methods, for stretching the fabrics during the sewing, which is based on neural networks while using qualitative assessment of the properties and characteristics of fabrics, is presented. Therefore, the concept of using linguistic variables for the qualitative description of the properties of fabrics, is introduced. In this context, it is the first time where the concept of the "extensibility" of a fabric is expressed quantitatively as a percentage. This is an essential and determining property of a fabric that should be taken into account during the sewing process. Also, the development of a new method for the automated experimental determination of "extensibility" of fabrics is described. This original approach has been applied to single and double layers of fabrics, while the tensile experiments are ‘intelligently’ incorporated into the same sewing machine and just before the sewing process. A fuzzy system for determining the desired tension for each of the fabrics is presented. This system is based on a qualitative assessment of "extensibility" of each fabric and it is initialized by the experience of the experts and the fuzzy implication they make between "extensibility" and desired tension. A new method to control the tension of the fabric during the sewing is developed. A feedforward neural controller, to control the robotic gripper to maintain the tension of the fabric according to the respective desired, has been used. Additionally, an expansion of the proposed approach is presented, for the case of the neuro-control of the tension, which showed the controller’s ability of generalization to other similar tasks. Also, new research areas that have been opened in the field of human-robot cooperation for handling non-rigid and very flexible objects are stated. Finally, there is presented a systematic experimental robotic execution of seams in a wide variety of both single and double layers of fabrics, while the, produced by the robot, seams are compared with the seams produced by the experts. Through experimental phase and the results of the stitched fabrics it is highlighted the desired flexibility of the system and its satisfactory response in a wide variety of fabrics.

Orientador: Marcos Julio Rider Flores / Resumo: Nesta tese são utilizados algoritmos de Inteligência Computacional para resolver quatro problemas da área de sistemas elétricos de potência, com o intuito de automatizar a tomada de decisões em processos que normalmente são realizados por especialistas humanos ajudados de métodos computacionais clássicos. Nesta tese são utilizados os algoritmos de aprendizado de máquina: árvores de decisão, redes neurais artificiais e máquinas de vetor de suporte, para realizar o processo de aprendizado dos sistemas inteligentes e para realizar a mineração de dados. Estes algoritmos podem ser treinados a partir das medições disponíveis e ações registradas nos centros de controle dos sistemas de potência. Sistemas Inteligentes foram utilizados para realizar: a) o controle centralizado Volt-VAr em modernos sistemas de distribuição de energia elétrica em tempo real usando medições elétricas; b) a detecção de fraudes nas redes de distribuição de energia elétrica realizando um processo de mineração de dados para estabelecer padrões de consumo que levem a possíveis clientes fraudadores; c) a localização de faltas nos sistemas de transmissão de energia elétrica automatizando o processo de localização e ajudando para que uma ação de controle da falta seja realizada de forma rápida e eficiente; e d) a coordenação de carga inteligente de veículos elétricos e dispositivos de armazenamento em tempo real utilizando a tecnologia V2G, nos sistemas de distribuição de energia elétrica a partir de medições elé... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: In this thesis Computational Intelligence algorithms are used to solve four problems of the area of power electrical systems, in order to automate decision making in processes that are usually performed by human experts aided by classical computational methods. In this thesis the machine learning algorithms are used: decision trees, artificial neural networks and support vector machines to carry out the learning process of Intelligent Systems and to perform Data Mining. These algorithms are trained from the available measurements and actions recorded in the control centers of the systems. Intelligent Systems were used to perform: a) the centralized control Volt-VAr in modern systems of distribution of electrical energy in real time using electrical measurements; b) detection of fraud in electricity distribution networks by performing a data mining process to establish patterns of consumption that lead to possible fraudulent customers; c) fault location in electric power transmission systems by automating the localization process and helping to ensure that a fault control action is performed quickly and efficiently; and d) coordination of intelligent charging of electric vehicles and storage devices using V2G technology in real-time, in electric power distribution systems using electrical measurements. For the centralized control problem Volt-VAr was tested in 42-node distribution system, for the problem of loading electric vehicles and storage devices the tests were performed... (Complete abstract click electronic access below) / Doutor

[pt] Colunas de aço estaiadas e protendidas são conhecidas como excelente solução em escoramento de grandes estruturas, como colunas de coberturas de lonas tensionadas, etc. Este trabalho apresenta uma série de ensaios experimentais tridimensionais em escala real desenvolvidos para determinação do comportamento estrutural de colunas de aço estaiadas e protendidas. Foram estudadas as variações no nível de protensão e na rigidez dos estais, além de descrever como os ensaios experimentais das três colunas de aço estaiadas são realizados: compreendendo os materiais utilizados; um novo sistema de medição de força nos estais; os passos e dificuldades na montagem das três colunas e o sistema de protensão aplicado. Existem alguns parâmetros que influenciam diretamente na resistência dessas colunas estaiadas, como por exemplo, a altura da coluna, o diâmetro externo, entre outros. Devido ao comportamento complexo deste tipo de colunas e ao grande esforço computacional para simulação do comportamento estrutural, através de uma análise paramétrica, optou-se por utilizar um projeto de experimentos junto com redes neurais a fim de extrapolar e obter novos resultados para carga crítica do sistema estrutural sem a necessidade de análise por programas de elementos finitos. De forma a complementar a tese, realizou-se um estudo do comportamento do sistema estrutural sujeito a ações dinâmicas através do programa de elementos finitos ANSYS com o objetivo de determinar as frequências naturais associadas aos seus modos de vibração. Também foi estudada a aplicação de um carregamento súbito para determinação do fator de amplificação dinâmico da coluna de aço estaiada e protendida. / [en] Prestressed steel columns are known as an efficient structural solution for great variety of temporary or permanent supporting systems for large span spatial frames and tensile surface structures. This work presents of full-scale three-dimensional tests carriedout for the assessment of structural behaviour of prestressed stayed steel columns. It was studied the effect prestress force level, stiffness of column braces and stays. Test setup and a new force measuring system for the column stays is fully described. Prestressed stayed steel columns have their strength dependant of parameters like: length, hollow section diameter, brace length and stiffness and axial stiffness of stays. Due to the complex behaviour of such columns that demands great computational effort for numerical simulations required for a parametric analysis it was used an experiment design tool coupled with neural network techniques employed to generate new data for the prestressed column buckling load. A study of the dynamic behaviour of prestressed columns using the finite element package ANSYS was carried-out in order to determine the column natural frequencies and their associated vibration modes. It was also studied the application of sudden loads to determine the dynamic amplification factor of this type of prestressed stayed steel column.

Indiana University-Purdue University Indianapolis (IUPUI) / Proteins are the building blocks in a biological system. They interact with other proteins to make unique biological phenomenon. Protein-protein interactions play a valuable role in understanding the molecular mechanisms occurring in any biological system. Protein interaction databases are a rich source on protein interaction related information. They gather large amounts of information from published literature to enrich their data. Expert curators put in most of these efforts manually. The amount of accessible and publicly available literature is growing very rapidly. Manual annotation is a time consuming process. And with the rate at which available information is growing, it cannot be dealt with only manual curation. There need to be tools to process this huge amounts of data to bring out valuable gist than can help curators proceed faster. In case of extracting protein-protein interaction evidences from literature, just a mere mention of a certain protein by look-up approaches cannot help validate the interaction. Supporting protein interaction information with experimental evidence can help this cause. In this study, we are applying machine learning based classification techniques to classify and given protein interaction related document into an interaction detection method. We use biological attributes and experimental factors, different combination of which define any particular interaction detection method. Then using predicted detection methods, proteins identified using named entity recognition techniques and decomposing the parts-of-speech composition we search for sentences with experimental evidence for a protein-protein interaction. We report an accuracy of 75.1% with a F-score of 47.6% on a dataset containing 2035 training documents and 300 test documents.

Indiana University-Purdue University Indianapolis (IUPUI) / Robustness and generalizability of supervised learning algorithms depend on the quality of the labeled data set in representing the real-life problem. In many real-world domains, however, we may not have full knowledge of the underlying data-generating mechanism, which may even have an evolving nature introducing new classes continually. This constitutes a partially-observed setting, where it would be impractical to obtain a labeled data set exhaustively defined by a fixed set of classes. Traditional supervised learning algorithms, assuming an exhaustive training library, would misclassify a future sample of an unobserved class with probability one, leading to an ill-defined classification problem. Our goal is to address situations where such assumption is violated by a non-exhaustive training library, which is a very realistic yet an overlooked issue in supervised learning. In this dissertation we pursue a new direction for supervised learning by defining self-adjusting models to relax the fixed model assumption imposed on classes and their distributions. We let the model adapt itself to the prospective data by dynamically adding new classes/components as data demand, which in turn gradually make the model more representative of the entire population. In this framework, we first employ suitably chosen nonparametric priors to model class distributions for observed as well as unobserved classes and then, utilize new inference methods to classify samples from observed classes and discover/model novel classes for those from unobserved classes. This thesis presents the initiating steps of an ongoing effort to address one of the most overlooked bottlenecks in supervised learning and indicates the potential for taking new perspectives in some of the most heavily studied areas of machine learning: novelty detection, online class discovery and semi-supervised learning.

The analysis of wide-angle cellular light scattering patterns is a challenging problem. Small changes to the organization, orientation, shape, and optical properties of scatterers and scattering populations can significantly alter their complex two-dimensional scattering signatures. Because of this, it is difficult to find methods that can identify medically relevant cellular properties while remaining robust to experimental noise and sample-to-sample differences. It is an important problem. Recent work has shown that changes to the internal structure of cells---specifically, the distribution and aggregation of organelles---can indicate the progression of a number of common disorders, ranging from cancer to neurodegenerative disease, and can also predict a patient's response to treatments like chemotherapy. However, there is no direct analytical solution to the inverse wide-angle cellular light scattering problem, and available simulation and interpretation methods either rely on restrictive cell models, or are too computationally demanding for routine use. This dissertation addresses these challenges from a computational vantage point. First, it explores the theoretical limits and optical basis for wide-angle scattering pattern analysis. The result is a rapid new simulation method to generate realistic organelle scattering patterns without the need for computationally challenging or restrictive routines. Pattern analysis, image segmentation, machine learning, and iterative pattern classification methods are then used to identify novel relationships between wide-angle scattering patterns and the distribution of organelles (in this case mitochondria) within a cell. Importantly, this work shows that by parameterizing a scattering image it is possible to extract vital information about cell structure while remaining robust to changes in organelle concentration, effective size, and random placement. The result is a powerful collection of methods to simulate and interpret experimental light scattering signatures. This gives new insight into the theoretical basis for wide-angle cellular light scattering, and facilitates advances in real-time patient care, cell structure prediction, and cell morphology research.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Derived from the field of art curation, digital provenance is an unforgeable record of a digital object's chain of successive custody and sequence of operations performed on the object. It plays an important role in accessing the trustworthiness of the object, verifying its reliability and conducting audit trails of its lineage. Digital provenance forms an immutable directed acyclic graph (DAG) structure. Since history of an object cannot be changed, once a provenance chain has been created it must be protected in order to guarantee its reliability. Provenance can face attacks against the integrity of records and the confidentiality of user information, making security an important trait required for digital provenance. The digital object and its associated provenance can have different security requirements, and this makes the security of provenance different from that of traditional data. Research on digital provenance has primarily focused on provenance generation, storage and management frameworks in different fields. Security of digital provenance has also gained attention in recent years, particularly as more and more data is migrated in cloud environments which are distributed and are not under the complete control of data owners. However, there still lacks a viable secure digital provenance scheme which can provide comprehensive security for digital provenance, particularly for generic and dynamic ones. In this work, we address two important aspects of secure digital provenance that have not been investigated thoroughly in existing works: 1) capturing the DAG structure of provenance and 2) supporting dynamic information sharing. We propose a scheme that uses signature-based mutual agreements between successive users to clearly delineate the transition of responsibility of the digital object as it is passed along the chain of users. In addition to preserving the properties of confidentiality, immutability and availability for a digital provenance chain, it supports the representation of DAG structures of provenance. Our scheme supports dynamic information sharing scenarios where the sequence of users who have custody of the document is not predetermined. Security analysis and empirical results indicate that our scheme improves the security of the typical secure provenance schemes with comparable performance.