401 research outputs found
Privacy-Preserving Clustering of Unstructured Big Data for Cloud-Based Enterprise Search Solutions
Cloud-based enterprise search services (e.g., Amazon Kendra) are enchanting
to big data owners by providing them with convenient search solutions over
their enterprise big datasets. However, individuals and businesses that deal
with confidential big data (eg, credential documents) are reluctant to fully
embrace such services, due to valid concerns about data privacy. Solutions
based on client-side encryption have been explored to mitigate privacy
concerns. Nonetheless, such solutions hinder data processing, specifically
clustering, which is pivotal in dealing with different forms of big data. For
instance, clustering is critical to limit the search space and perform
real-time search operations on big datasets. To overcome the hindrance in
clustering encrypted big data, we propose privacy-preserving clustering schemes
for three forms of unstructured encrypted big datasets, namely static,
semi-dynamic, and dynamic datasets. To preserve data privacy, the proposed
clustering schemes function based on statistical characteristics of the data
and determine (A) the suitable number of clusters and (B) appropriate content
for each cluster. Experimental results obtained from evaluating the clustering
schemes on three different datasets demonstrate between 30% to 60% improvement
on the clusters' coherency compared to other clustering schemes for encrypted
data. Employing the clustering schemes in a privacy-preserving enterprise
search system decreases its search time by up to 78%, while increases the
search accuracy by up to 35%.Comment: arXiv admin note: text overlap with arXiv:1908.0496
DESIGN AND EXPLORATION OF NEW MODELS FOR SECURITY AND PRIVACY-SENSITIVE COLLABORATION SYSTEMS
Collaboration has been an area of interest in many domains including education, research, healthcare supply chain, Internet of things, and music etc. It enhances problem solving through expertise sharing, ideas sharing, learning and resource sharing, and improved decision making.
To address the limitations in the existing literature, this dissertation presents a design science artifact and a conceptual model for collaborative environment. The first artifact is a blockchain based collaborative information exchange system that utilizes blockchain technology and semi-automated ontology mappings to enable secure and interoperable health information exchange among different health care institutions. The conceptual model proposed in this dissertation explores the factors that influences professionals continued use of video- conferencing applications. The conceptual model investigates the role the perceived risks and benefits play in influencing professionals’ attitude towards VC apps and consequently its active and automatic use
Investigating Context Awareness of Affective Computing Systems: A Critical Approach
AbstractIntelligent Human Computer Interaction systems should be affective aware and Affective Computing systems should be context aware. Positioned in the cross-section of the research areas of Interaction Context and Affective Computing current paper investigates if and how context is incorporated in automatic analysis of human affective behavior. Several related aspects are discussed ranging from modeling, acquiring and annotating issues in affectively enhanced corpora to issues related to incorporating context information in a multimodal fusion framework of affective analysis. These aspects are critically discussed in terms of the challenges they comprise while, in a wider framework, future directions of this recently active, yet mainly unexplored, research area are identified. Overall, the paper aims to both document the present status as well as comment on the evolution of the upcoming topic of Context in Affective Computing
DREQUS: an approach for the Discovery of REQuirements Using Scenarios
ABSTRACT: Requirements engineering is recognized as a complex cognitive problem-solving process that takes place in an unstructured and poorly-understood problem context. Requirements elicitation is the activity generally regarded as the most crucial step in the requirements engineering process. The term “elicitation” is preferred to “capture”, to avoid the suggestion that requirements are out there to be collected. Information gathered during requirements elicitation often has to be interpreted, analyzed, modeled, and validated before the requirements engineer can feel confident that a complete set of requirements of a system have been obtained. Requirements elicitation comprises the set of activities that enable discovering, understanding, and documenting the goals and motives for building a proposed software system. It also involves identifying the requirements that the resulting system must satisfy in to achieve these goals. The requirements to be elicited may range from modifications to well-understood problems and systems (i.e. software upgrades), to hazy understandings of new problems being automated, to relatively unconstrained requirements that are open to innovation (e.g. mass-market software). Requirements elicitation remains problematic; missing or mistaken requirements still delay projects and cause cost overruns. No firm definition has matured for requirements elicitation in comparison to other areas of requirements engineering. This research is aimed to improve the results of the requirements elicitation process directly impacting the quality of the software products derived from them
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
D7.4 Third evaluation report. Evaluation of PANACEA v3 and produced resources
D7.4 reports on the evaluation of the different components integrated in the PANACEA third cycle of development as well as the final validation of the platform itself. All validation and evaluation experiments follow the evaluation criteria already described in D7.1. The main goal of WP7 tasks was to test the (technical) functionalities and capabilities of the middleware that allows the integration of the various resource-creation components into an interoperable distributed environment (WP3) and to evaluate the quality of the components developed in WP5 and WP6. The content of this deliverable is thus complementary to D8.2 and D8.3 that tackle advantages and usability in industrial scenarios. It has to be noted that the PANACEA third cycle of development addressed many components that are still under research. The main goal for this evaluation cycle thus is to assess the methods experimented with and their potentials for becoming actual production tools to be exploited outside research labs. For most of the technologies, an attempt was made to re-interpret standard evaluation measures, usually in terms of accuracy, precision and recall, as measures related to a reduction of costs (time and human resources) in the current practices based on the manual production of resources. In order to do so, the different tools had to be tuned and adapted to maximize precision and for some tools the possibility to offer confidence measures that could allow a separation of the resources that still needed manual revision has been attempted. Furthermore, the extension to other languages in addition to English, also a PANACEA objective, has been evaluated. The main facts about the evaluation results are now summarized
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