Provenance-Based Attestation for Trustworthy Computing

This thesis describes a data provenance framework and other associated frameworks for utilizing provenance for data quality and reproducibility. We first identify the requirements for the design of a comprehensive provenance framework which can be applicable to various applications, supports a rich set of provenance metadata, and is interoperable with other provenance management systems. We then design and develop a provenance framework, called SimP, addressing such requirements. Next, we present four prominent applications and investigate how provenance data can be beneficial to such applications. The first application is the quality assessment of access control policies. Towards this, we design and implement the ProFact framework which uses provenance techniques for collecting comprehensive data about actions which were either triggered due to a network context or a user (i.e., a human or a device) action. Provenance data are used to determine whether the policies meet the quality requirements. ProFact includes two approaches for policy analysis: structure-based and classification-based. For the structure-based approach, we design tree structures to organize and assess the policy set efficiently. For the classification-based approach, we employ several classification techniques to learn the characteristics of policies and predict their quality. In addition, ProFact supports policy evolution and the assessment of its impact on the policy quality. The second application is workflow reproducibility. Towards this, we implement ProWS which is a provenance-based architecture for retrieving workflows. Specifically, ProWS transforms data provenance into workflows and then organizes data into a set of indexes to support efficient querying mechanisms. ProWS supports composite queries on three types of search criteria: keywords of workflow tasks, patterns of workflow structure, and metadata about workflows (e.g., how often a workflow was used). The third application is the access control policy reproducibility. Towards this, we propose a novel framework, Polisma, which generates attribute-based access control policies from data, namely from logs of historical access requests and their corresponding decisions. Polisma combines data mining, statistical, and machine learning techniques, and capitalizes on potential context information obtained from external sources (e.g., LDAP directories) to enhance the learning process. The fourth application is the policy reproducibility by utilizing knowledge and experience transferability. Towards this, we propose a novel framework, FLAP, which transfer attribute-based access control policies between different parties in a collaborative environment, while considering the challenges of minimal sharing of data and support policy adaptation to address conflict. All frameworks are evaluated with respect to performance and accuracy.

The state-of-the-art for auditing and reproducing scientific applications on high-performance computing (HPC) systems is through a data provenance subsystem. While recent advances in data provenance lie in reducing the performance overhead and improving the user's query flexibility, the fidelity of data provenance is often overlooked: there is no such way to ensure that the provenance data itself has not been fabricated or falsified. This paper advocates leveraging blockchains to deliver immutable and autonomous data provenance services such that scientific discoveries are trustworthy. The challenges for adopting blockchains to HPC include designing a new blockchain architecture compatible with the HPC platforms and, more importantly, a set of new consensus protocols for scientific applications atop blockchains. To this end, we have designed the proof-of-scalable-traceability (POST) protocol and implemented it in a blockchain prototype, namely SciChain, the very first practical blockchain system for provenance services on HPC. We evaluated SciChain by comparing it with multiple state-of-the-art systems; experimental results showed that SciChain guaranteed trustworthy data provenance while incurring orders of magnitude lower overhead than existing solutions.

Data provenance collects comprehensive information about the events and operations in a computer system at both application and kernel levels. It provides a detailed and accurate history of transactions that help delineate the data flow scenario across the whole system. Data provenance helps achieve system resilience by uncovering several malicious attack traces after a system compromise that are leveraged by the analyzer to understand the attack behavior and discover the level of damage. Existing literature demonstrates a number of research efforts on information capture, management, and analysis of data provenance. In recent years, provenance in IoT devices attracts several research efforts because of the proliferation of commodity IoT devices. In this survey paper, we present a comparative study of the state-of-the-art approaches to provenance by classifying them based on frameworks, deployed techniques, and subjects of interest. We also discuss the emergence and scope of data provenance in IoT network. Finally, we present the urgency in several directions that data provenance needs to pursue, including data management and analysis.

In the Big Data era, data provenance has become an important concern for enhancing the trustworthiness of key data that are rapidly generated and shared across organizations. Prevailing solutions employ authoritative centers to efficiently manage and share massive data. They are not suitable for secure and trustworthy decentralized data provenance sharing due to the inevitable dishonesty or failure of trusted centers. With the advent of the blockchain technology, embedding data provenance in immutable blocks is believed to be a promising solution. However, a provenance file, usually a directed acyclic graph, cannot be embedded in blocks as a whole because its size may exceed the limit of a block, and may include various sensitive information that can be legally accessed by different users. To this end, this paper proposed the BSTProv, a blockchain-based system for secure and trustworthy decentralized data provenance sharing. It enables secure and trustworthy provenance sharing by partitioning a large provenance graph into multiple small subgraphs and embedding the encrypted subgraphs instead of raw subgraphs or their hash values into immutable blocks of a consortium blockchain; it enables decentralized and flexible authorization by allowing each peer to define appropriate permissions for selectively sharing some sets of subgraphs to specific requesters; and it enables efficient cross-domain provenance composition and tracing by maintaining a high-level dependency structure among provenance graphs from different domains in smart contracts, and by locally storing, decrypting, and composing subgraphs obtained from the blockchain. Finally, a prototype is implemented on top of an Ethereum-based consortium blockchain and experiment results show the advantages of our approach.

Cloud computing is the next generation computer system which extends network architecture into dynamic and large scale capacity by using visualization techniques. Transparent and secure data provenance will enhance the level of trust in the Cloud. Recent literature on securing data provenance focuses only on providing partial part of security elements in their mechanisms, thus they could not provide full protection to the data provenance as a whole. This paper presents the provenance description and challenges in providing security assurances in the Cloud. The paper also proposes a novel trust model for data provenance in cloud computing. The model will be used for securing the transaction process of storing and accessing the data provenance. The paper describes in detail the flow process of the model to achieve a high level of trust in cloud services. The significance of this research is to enhance the level of trust in the cloud services via comprehensive trust model which consists all aspects of security elements in the model.

In modern cryptography, hash functions are considered as one of the key components for secure communication. They play a vital role in a wide range of applications such as ensuring the authentication and integrity of the data, in forensic investigation, password storage, random number generations for unique session keys, and for creating a unified view in blockchain. The Avalanche effect (also known as diffusion) is an important characteristic of a hash function where a minor change in the hash function’s input will result in a significantly different output. The absence of this property implies that the hash function is vulnerable to various attacks such as collision attack, length extension attack, and preimage attack. Through this research, we have investigated the Avalanche effect of sixteen hash functions and two hash-based applications, namely Hash-based Message Authentication Code (HMAC) and Public Key Cryptography Standards (PKCS). To measure the performance of these hash functions and hash-based applications, we have implemented a generic circuit using CrypTool for automating the simulation process for multiple trials. Simulation results indicate that around half of the inputs of each hash function failed to exhibit the Strict Avalanche Criterion (SAC) and, Bit Independence Criterion (BIC). Moreover, we ranked the hash functions based on Multi Criteria Decision Metrics (MCDM) using intermediate states of simulation results. Furthermore, a total of fifteen statistical tests were carried out to evaluate the randomization property of the hash functions using NIST (National Institute of Standards and Technology) toolkit. This study is aimed to open up a future scope of research to the need for improvement of various hash functions by analyzing the randomization and non-correlation properties of existing functions in terms of the Avalanche effect.

Characterizing workflow-based activity on a production e-infrastructure using provenance data

This paper analyzes the computer security of systems and importance of the digital signature and hashing message algorithm. The proposed digital signature algorithm gives a new technology for producing effective output of digital signature as a result the signing 1 and verifying of signatures are very fast compared to earlier ones. To improve the security and authentication of sending data, this method uses "Message Digest", "IDEA" and "GOST" 2 algorithms. The new message digest algorithm is to provide high security, to transfer data by combination of digital signature algorithm and symmetric key cryptography algorithm. The new hashing algorithm proposed creates a unique digital fingerprint along with symmetric key encryption generated IDEA and GOST algorithms 3,4 . The receiver used the symmetric key and hashing algorithm to form a signature. If this message digest match with the sender digests the message the content will be decrypted and read by sender.

Landscape is constructed through the diverse activities of individuals in the environment that surrounds them. In this article, we address the role that coastal and lagoonal areas played in the way in which the human communities that inhabited southern Sardinia during the Final Neolithic constructed their landscape. To this end, we approach how individuals managed the mineral raw materials available in these locations and constructed taskspaces embedded in the landscape. We studied the provenance of the clays used to make diverse types of artifacts (i.e., pottery, coatings on architectural structures and loom weights) using optical microscopy based on thin-section analysis. In addition, microfossils present in the samples have been identified. The link of these archaeological materials with certain sedimentary deposits from the surrounding area enables us to suggest a close interrelationship and knowledge exchange between different technologies with respect to the possibilities of the diverse resources available in the environment and the elements that formed the landscape of these communities. Thus, the preference to exploit raw materials from lagoonal and coastal areas demonstrates that these environments became a reference point in the construction of landscape and identity in these communities.

Hash functions are established and well-known in digital forensics, where they are commonly used for proving integrity and file identification (i.e., hash all files on a seized device and compare the fingerprints against a reference database). However, with respect to the latter operation, an active adversary can easily overcome this approach because traditional hashes are designed to be sensitive to altering an input; output will significantly change if a single bit is flipped. Therefore, researchers developed approximate matching, which is a rather new, less prominent area but was conceived as a more robust counterpart to traditional hashing. Since the conception of approximate matching, the community has constructed numerous algorithms, extensions, and additional applications for this technology, and are still working on novel concepts to improve the status quo. In this survey article, we conduct a high-level review of the existing literature from a non-technical perspective and summarize the existing body of knowledge in approximate matching, with special focus on bytewise algorithms. Our contribution allows researchers and practitioners to receive an overview of the state of the art of approximate matching so that they may understand the capabilities and challenges of the field. Simply, we present the terminology, use cases, classification, requirements, testing methods, algorithms, applications, and a list of primary and secondary literature.

This archive contains the dump of the OpenCitation Corpus (OCC, http://opencitations.net) provenance data about author roles, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.

This archive contains the dump of the OpenCitation Corpus (OCC, http://opencitations.net) provenance data about identifiers, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.

This archive contains the dump of the OpenCitation Corpus (OCC, http://opencitations.net) provenance data about resource embodiments, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.

i»? A. A. Helal, A. A. Heddaya, and B. B. Bhargrava Kluwer Academic Publishers, Boston, 1996, 176 pp. ISBN 0-7923-9800-9, $118.50 Computing in the 1990s has reached the state of distributed computing. A basis of this form of computing is a distributed computing system, which is built on the following three components: (a) personal computers, (b) local and fast wide area networks, and (c) system and application software. By amalgamating computers and networks into one single computing system and providing an appropriate system software, a distributed computing system has created the possibility of sharing information and peripheral resources. Furthermore, these systems improved performance of a computing system and individual users through parallel execution of programs, load balancing and sharing, and replication of programs and data. Distributed computing systems are also characterized by enhanced availability, and increased reliability. Replication is the key to providing high availability, fault tolerance, and enhanced performance in a distributed computing system. As companies move toward systems that are more open and distributed, replication is becoming increasingly important in the ability to provide data and services that are current, correct and available, which is a key factor in maintaining a competitive advantage over rivals. However, replication has also generated some serious challenges. For example, when a replica is updated, how do we propagate such an update to other replicas? If multiple replicas are updated simultaneously and some of the updates conflict one another, how do we resolve the conflict? How do we deal with the situation where a replica is down and then subsequently recovered? How do we deal with network partitions? Not surprisingly, considerable research efforts have been directed towards the solution of these challenges. However, most of the research results are scattered among many journals, conference proceedings, theses, and technical reports. The book by Helal, Heddaya and Bhargava has gathered the best material of these research results and formed a coherent collection that includes definitions, theoretical background, algorithms, annotated bibliographies of commercial and experimental prototype systems, and more than 200 references. This book classifies the entities that can be replicated on a distributed computing system into the following categories: (a) data, (b) process, (c) objects, and (d) messages. After the introduction on the goals and main approaches of replication techniques, the book uses individual chapters to describe major methods for each of the above replication categories. The main focus of this part, however, is on the replication of data. The book also contains two chapters on the replication issues in heterogeneous, mobile, and large-scale systems, and on the future of replication techniques. Apart from the normal chapters, the book contains a rich set of appendices that are very useful for further studies of replication techniques. These appendices include brief descriptions of two dozen commercial and experimental systems that use replication techniques, annotated bibliographies of selected literature on various topics of replication, and an introduction on serializability theory. Each annotated bibliography contains an overview of the specific topic and is written by an invited expert in that field. The combination of the normal chapters and appendices cover the entire spectrum of replication, from definitions, concepts, theories, algorithms, techniques, to systems. The book serves as an excellent introduction and roadmap to the issues of replication. Although it does not provide a detailed description of every replication techniques and systems, the book covers most of the fundamental work that allows the reader to understand the roots of these techniques and systems, and to find the ways to search for the details. This book is a valuable reference book for anyone studying replication techniques or implementing replication systems, specifically, it can be very useful to post-graduate students, practitioners, and beginning researchers of this exciting field. Wanlei Zhou, School of Computing and Mathematics, Deakin University

Learners benefit from collaboration because it triggers effective interaction processes such as externalization, elicitation and negotiation of knowledge. In order to communicate effectively, learners need to have a certain representation of their peers' knowledge. We refer to the process of building and maintaining a representation of the peers' knowledge as peer knowledge modeling. The present thesis aim at making contributions to the fields of computer support collaborative learning and work (CSCL, CSCW) on three main levels. First, as an empirical contribution, we investigate the process of peer knowledge modeling in the context of CSCL. Our main research question inquires the effects of a socio-cognitive support, providing co-learners with cues about their peer's prior knowledge, on collaborative learning outcomes and processes. In an empirical study (the KAT experiment), university students (N=64) participated in a remote computer-mediated dyadic learning scenario. They were provided (or not) with a visual representation of their partner's prior-knowledge level through a Knowledge Awareness Tool (KAT). Results showed that the KAT enhances co-learners' collaborative learning gain. This effect appears to be mediated by the positive effect of the KAT on participants' accuracy in estimating their peer's knowledge. Analyses on the process level showed that participants of the KAT condition produce more elaborated utterances. KAT condition dyads' interactions are more focused on knowledge negotiation, whereas the control condition dyads are mainly focused on task completion. The KAT seems to provide a sensitizing metacognitive support, structuring and regulating the collaboration by helping co-learners to cope with their knowledge gaps and discrepancies. Second, as a methodological contribution, we examine the affordance of dual eye tracking techniques as an innovative methodology to investigate, on a deeper level, the socio-cognitive processes underlying collaboration. We introduce DUET (DUal Eye-Tracking), a method using a multimodal technique to collect rich data featuring peers' synchronized gaze patterns, verbal interaction and potentially activities. We examine the main research applications of DUET and exemplify them with analyses conducted in the context of the KAT experiment. DUET method appears to be a promising technique to investigate collaborative processes on a deep level. Finally, as a third computational contribution, we built upon micro-level analyses of the verbal referencing process to introduce and test REGARD, a computational model allowing to automatically detect verbal references and locate the specific object of reference. The results of the test show a reasonably good accuracy of the REGARD algorithm to detect and associate verbal references to objects of reference. We discuss the design and research application of the REGARD model for the field of CSCL, CSCW and more generally HCI.