2,061 research outputs found

    E-Tenon: An efficient privacy-preserving secure open data sharing scheme for EHR system

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    The transition from paper-based information to Electronic-Health-Records (EHRs) has driven various advancements in the modern healthcare industry. In many cases, patients need to share their EHR with healthcare professionals. Given the sensitive and security-critical nature of EHRs, it is essential to consider the security and privacy issues of storing and sharing EHR. However, existing security solutions excessively encrypt the whole database, thus requiring the entire database to be decrypted for each access request, which is time-consuming. On the other hand, the use of EHR for medical research (e.g., development of precision medicine and diagnostics techniques) and optimisation of practices in healthcare organisations require the EHR to be analysed. To achieve that, they should be easily accessible without compromising the patient’s privacy. In this paper, we propose an efficient technique called E-Tenon that not only securely keeps all EHR publicly accessible but also provides the desired security features. To the best of our knowledge, this is the first work in which an Open Database is used for protecting EHR. The proposed E-Tenon empowers patients to securely share their EHR under their own multi-level, fine-grained access policies. Analyses show that our system outperforms existing solutions in terms of computational complexity

    Displacement and the Humanities: Manifestos from the Ancient to the Present

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    This is the final version. Available on open access from MDPI via the DOI in this recordThis is a reprint of articles from the Special Issue published online in the open access journal Humanities (ISSN 2076-0787) (available at: https://www.mdpi.com/journal/humanities/special_issues/Manifestos Ancient Present)This volume brings together the work of practitioners, communities, artists and other researchers from multiple disciplines. Seeking to provoke a discourse around displacement within and beyond the field of Humanities, it positions historical cases and debates, some reaching into the ancient past, within diverse geo-chronological contexts and current world urgencies. In adopting an innovative dialogic structure, between practitioners on the ground - from architects and urban planners to artists - and academics working across subject areas, the volume is a proposition to: remap priorities for current research agendas; open up disciplines, critically analysing their approaches; address the socio-political responsibilities that we have as scholars and practitioners; and provide an alternative site of discourse for contemporary concerns about displacement. Ultimately, this volume aims to provoke future work and collaborations - hence, manifestos - not only in the historical and literary fields, but wider research concerned with human mobility and the challenges confronting people who are out of place of rights, protection and belonging

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Auditable and performant Byzantine consensus for permissioned ledgers

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    Permissioned ledgers allow users to execute transactions against a data store, and retain proof of their execution in a replicated ledger. Each replica verifies the transactions’ execution and ensures that, in perpetuity, a committed transaction cannot be removed from the ledger. Unfortunately, this is not guaranteed by today’s permissioned ledgers, which can be re-written if an arbitrary number of replicas collude. In addition, the transaction throughput of permissioned ledgers is low, hampering real-world deployments, by not taking advantage of multi-core CPUs and hardware accelerators. This thesis explores how permissioned ledgers and their consensus protocols can be made auditable in perpetuity; even when all replicas collude and re-write the ledger. It also addresses how Byzantine consensus protocols can be changed to increase the execution throughput of complex transactions. This thesis makes the following contributions: 1. Always auditable Byzantine consensus protocols. We present a permissioned ledger system that can assign blame to individual replicas regardless of how many of them misbehave. This is achieved by signing and storing consensus protocol messages in the ledger and providing clients with signed, universally-verifiable receipts. 2. Performant transaction execution with hardware accelerators. Next, we describe a cloud-based ML inference service that provides strong integrity guarantees, while staying compatible with current inference APIs. We change the Byzantine consensus protocol to execute machine learning (ML) inference computation on GPUs to optimize throughput and latency of ML inference computation. 3. Parallel transactions execution on multi-core CPUs. Finally, we introduce a permissioned ledger that executes transactions, in parallel, on multi-core CPUs. We separate the execution of transactions between the primary and secondary replicas. The primary replica executes transactions on multiple CPU cores and creates a dependency graph of the transactions that the backup replicas utilize to execute transactions in parallel.Open Acces

    Ministry through COVID and Beyond

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    Perceptions and Practicalities for Private Machine Learning

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    data they and their partners hold while maintaining data subjects' privacy. In this thesis I show that private computation, such as private machine learning, can increase end-users' acceptance of data sharing practices, but not unconditionally. There are many factors that influence end-users' privacy perceptions in this space; including the number of organizations involved and the reciprocity of any data sharing practices. End-users emphasized the importance of detailing the purpose of a computation and clarifying that inputs to private computation are not shared across organizations. End-users also struggled with the notion of protections not being guaranteed 100\%, such as in statistical based schemes, thus demonstrating a need for a thorough understanding of the risk form attacks in such applications. When training a machine learning model on private data, it is critical to understand the conditions under which that data can be protected; and when it cannot. For instance, membership inference attacks aim to violate privacy protections by determining whether specific data was used to train a particular machine learning model. Further, the successful transition of private machine learning theoretical research to practical use must account for gaps in achieving these properties that arise due to the realities of concrete implementations, threat models, and use cases; which is not currently the case

    “Down the rabbit hole” Exploring the role of Psychopathological and Socio-Cognitive Factors in Conspiracy Theory Beliefs and Stratergies for Intervention.

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    The spread of conspiracy theories and misinformation has become increasingly common in recent years, particularly becoming a rather topical area on social media platforms since the emergence of the COVID-19 pandemic. Such theories have been linked to several negative social and health consequences, leading to them becoming a topic of interest for researchers. Research in psychology has focused on factors that contribute to the adoption of conspiracy theories from various levels of approach. This thesis explores the individual differences that may contribute to how conspiracy information is evaluated, and, in turn, may explain why conspiracy beliefs are endorsed as well as the implications for challenging these belief systems. To this end, I conducted five studies which examined various individual differences, some yet to be explored in the conspiracy theory literature. A particular focus was to extend the range of clinical measures considered in this area, and, to develop a greater understanding of cognitive factors related to conspiracy beliefs through a more integrated approach (e.g., the inclusion of multiple explanatory lines from research). Following the introductory chapter reviewing the relevant existing literature, Chapter 3 presents Study One which focussed on the potential role of autistic traits as a confounding factor between the relationship between schizotypy and conspiracy beliefs. Chapter 4 reports differences in cognitive style, information seeking behaviour and conspiracy theory beliefs for those who scored above the clinical ASD cut-off compared to the rest of the sample. Chapter 5 presents a refined approach towards thinking styles and examined how people engage in the scientific appraisal of conspiracy information. Chapter 6 assessed the within-individual variation of schizotypy, autistic traits, socio-cognitive tendencies associated with conspiracy beliefs and scientific reasoning ability through a Latent Profile Analysis (LPA). Chapter 7 presents the fifth and final study, to which an intervention approach examined whether encouraging a stronger orientation toward critical scientific appraisal of conspiracy theories could reduce their acceptance. This thesis closes with a general discussion of how it has made a novel contribution to the area of conspiracy research and other related fields. Specifically, I discuss the theoretical and methodological contributions advanced by this thesis through the inclusion of novel psychopathological and socio-cognitive features, how such advancement improved our understanding of the different pathways which lead to conspiracy beliefs, then, how this research into conspiracy beliefs may represent a novel contribution to clinical research. One of the main contrutions being the significance of scientific reasoning skills as amenable to an intervention approach for conspiracy theory beliefs. I conclude with the implications of this work for future research and the conclusions that could be drawn from this thesis.Thesis (Ph.D.) -- University of Adelaide, School of Psychology, 202
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