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\"This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying formerly unused machine learning methodologies, such as supervised learning. The application of various learning algorithms, such as linear regression, matrix decomposition, and decision trees, illustrates how to translate the trust modeling problem into a (supervised) learning problem. The book also shows how novel machine learning techniques can improve the accuracy of trust assessment compared to traditional approaches\"-- Provided by publisher.

In traditional cloud storage systems, users benefit from the convenience of data accessibility but face significant risks related to security. Ciphertext-policy attribute-based encryption (CP-ABE) schemes are employed to achieve fine-grained access control in cloud services to ensure confidentiality while maintaining data-sharing capabilities. However, existing approaches are impaired by two critical issues: illegal authorization and privacy leakage. Despite extensive discussions in the literature on interoperability, performance, scalability, and stability, the security of ABE-based cloud storage and data-sharing systems against adversaries—particularly those involving adaptively corrupt attribute authorities gaining unauthorized access to users’ data—has not been sufficiently explored. Notably, few existing works even address security in the presence of adversaries, raising concerns about the practicality of these systems in real-world scenarios where malicious behavior is a genuine threat. Another pressing issue is privacy leakage, where sensitive user information, such as medical histories in healthcare use cases, embedded within the access policies, may be exposed to all users. This problem is exacerbated in ABE schemes that integrate blockchain technology for enhanced decentralization and interoperability, as using a public ledger shared across multiple users can further compromise privacy. To address these, we propose an enhanced blockchain-based data governance system that employs blockchain technology and attribute-based encryption to prevent illegal authorization and privacy leakage. Our novel ABE encryption system supports multi-authority use cases while hiding access policy and ensuring identity privacy, which also protects data sharing against corrupt authorities. Utilizing the Advanced Encryption Standard (AES) for data encryption, our system is optimized for real-world efficiency. Notably, the encrypted data is stored in a decentralized storage system, like the InterPlanetary File System (IPFS), which does not rely on any centralized service provider and can, therefore, be leveraged to achieve resilience against single-point failures. With the integration of smart contracts and multi-authority attribute-based encryption, coupled with blockchain’s inherent transparency and traceability, our system realizes a balanced solution for fine-grained access control with preserved privacy, further fortifying against credential misuse. Besides the system design, we also present security proofs to demonstrate the robustness of the proposed system.

This work is motivated by applications of parsimonious cladograms for the purpose of analyzing non-biological data. Parsimonious cladograms were introduced as a means to help understanding the tree of life, and are now used in fields related to biological sciences at large, e.g ., to analyze viruses or to predict the structure of proteins. We revisit parsimonious cladograms through the lens of clustering and compare cladograms optimized for parsimony with dendograms obtained from single linkage hierarchical clustering. We show that despite similarities in both approaches, there exist datasets whose clustering dendogram is incompatible with parsimony optimization. Furthermore, we provide numerical examples to compare via F-scores the clustering obtained through both parsimonious cladograms and single linkage hierarchical dendograms.

We consider the problem of designing grid quorum systems for maximum distance separable (MDS) erasure code based distributed storage systems. Quorums are used as a mechanism to maintain consistency in replication based storage systems, for which grid quorums have been shown to produce optimal load characteristics. This motivates the study of grid quorums in the context of erasure code based distributed storage systems. We show how grid quorums can be built for erasure coded data, investigate the load characteristics of these quorum systems, and demonstrate how sequential consistency is achieved even in the presence of storage node failures.

This article explores a graph clustering method that is derived from an information theoretic method that clusters points in${{\\mathbb{R}}^{n}}$relying on Renyi entropy, which involves computing the usual Euclidean distance between these points. Two view points are adopted: (1) the graph to be clustered is first embedded into${\\mathbb{R}}^{d}$for some dimension d so as to minimize the distortion of the embedding, then the resulting points are clustered, and (2) the graph is clustered directly, using as distance the shortest path distance for undirected graphs, and a variation of the Jaccard distance for directed graphs. In both cases, a hierarchical approach is adopted, where both the initial clustering and the agglomeration steps are computed using Renyi entropy derived evaluation functions. Numerical examples are provided to support the study, showing the consistency of both approaches (evaluated in terms of F -scores).

Even as data and analytics-driven applications are becoming increasingly popular, retrieving data from shared databases poses a threat to the privacy of their users. For example, investors/patients retrieve records about stocks/diseases they are interested in from a stock/medical database. Knowledge of such interest is sensitive information that the database server would have access to, unless some mitigating measures are deployed. Private information retrieval (PIR) is a promising security primitive to protect the privacy of users’ interests. PIR allows the retrieval of a data record from a database without letting the database server know which record is being retrieved. The privacy guarantees could either be information theoretic or computational. Alternatively, anonymizers, which hide the identities of data users, may be used to protect the privacy of users’ interests for some situations. In this paper, we study rPIR, a new family of information-theoretic PIR schemes using ramp secret sharing. We have designed four rPIR schemes, using three ramp secret sharing approaches, achieving answer communication costs close to the cost of non-private information retrieval. Evaluation shows that, for many practical settings, rPIR schemes can achieve lower communication costs and the same level of privacy compared with traditional information-theoretic PIR schemes and anonymizers. Efficacy of the proposed schemes is demonstrated for two very different scenarios (outsourced data sharing and P2P content delivery) with realistic analysis and experiments. In many situations of these two scenarios, rPIR’s advantage of low communication cost outweighs its disadvantages, which results in less expenditure and/or better quality of service compared with what may be achieved if traditional information-theoretic PIR and anonymizers are used.

The notion of entropic centrality measures how central a node is in terms of how uncertain the destination of a flow starting at this node is: the more uncertain the destination, the more well connected and thus central the node is deemed. This implicitly assumes that the flow is indivisible, and at every node, the flow is transferred from one edge to another. The contribution of this paper is to propose a split-and-transfer flow model for entropic centrality, where at every node, the flow can actually be arbitrarily split across choices of neighbours. We show how to map this to an equivalent transfer entropic centrality set-up for the ease of computation, and carry out three case studies (an airport network, a cross-shareholding network and a Bitcoin transactions subnetwork) to illustrate the interpretation and insights linked to this new notion of centrality.

Data This study looks at the content on Reddit’s COVID-19 community, r/Coronavirus, to capture and understand the main themes and discussions around the global pandemic, and their evolution over the first year of the pandemic. It studies 356,690 submissions (posts) and 9,413,331 comments associated with the submissions, corresponding to the period of 20th January 2020 and 31st January 2021. Methodology On each of these datasets we carried out analysis based on lexical sentiment and topics generated from unsupervised topic modelling. The study found that negative sentiments show higher ratio in submissions while negative sentiments were of the same ratio as positive ones in the comments. Terms associated more positively or negatively were identified. Upon assessment of the upvotes and downvotes, this study also uncovered contentious topics, particularly “fake” or misleading news. Results Through topic modelling, 9 distinct topics were identified from submissions while 20 were identified from comments. Overall, this study provides a clear overview on the dominating topics and popular sentiments pertaining the pandemic during the first year. Conclusion Our methodology provides an invaluable tool for governments and health decision makers and authorities to obtain a deeper understanding of the dominant public concerns and attitudes, which is vital for understanding, designing and implementing interventions for a global pandemic.

This book provides a detailed introduction to the concept of trust and its application in various computer science areas. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this text effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment. It explains how reputation-based systems are used to determine trust in diverse online communities, discusses collaborative filtering-based trust aware recommendation systems, and investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions to ensure credibility.

Data outsourcing is plagued with several security and privacy concerns. Oblivious RAM (ORAM) can be used to address one of the many concerns, specifically to protect the privacy of data access pattern from outsourced cloud storage. This is achieved by simulating each original read or write operation with some read and write operations on both real and dummy data items. This paper proposes two single-server write-only ORAM schemes and one multi-server scheme, which simulate only the write operations and protect only the write pattern. The reduction in functionality however allows to build much simpler and efficient (in terms of communication/storage cost) ORAMs. Our schemes can achieve constant communication cost with acceptable storage usage. Write-only ORAM can be used in two situations: (i) only the write pattern is considered to contain sensitive information and needs protection. (ii) In outsourced data sharing, ORAM cannot be used to protect read pattern anyway due to access control issues, and Private Information Retrieval (PIR) has to be used instead. In this paper, we also study how to augment ORAM to support the use of PIR in the latter situation.