70,322 research outputs found
Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation
With the wide deployment of public cloud computing infrastructures, using
clouds to host data query services has become an appealing solution for the
advantages on scalability and cost-saving. However, some data might be
sensitive that the data owner does not want to move to the cloud unless the
data confidentiality and query privacy are guaranteed. On the other hand, a
secured query service should still provide efficient query processing and
significantly reduce the in-house workload to fully realize the benefits of
cloud computing. We propose the RASP data perturbation method to provide secure
and efficient range query and kNN query services for protected data in the
cloud. The RASP data perturbation method combines order preserving encryption,
dimensionality expansion, random noise injection, and random projection, to
provide strong resilience to attacks on the perturbed data and queries. It also
preserves multidimensional ranges, which allows existing indexing techniques to
be applied to speedup range query processing. The kNN-R algorithm is designed
to work with the RASP range query algorithm to process the kNN queries. We have
carefully analyzed the attacks on data and queries under a precisely defined
threat model and realistic security assumptions. Extensive experiments have
been conducted to show the advantages of this approach on efficiency and
security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201
Using Visualization to Support Data Mining of Large Existing Databases
In this paper. we present ideas how visualization technology can be used to improve the difficult process of querying very large databases. With our VisDB system, we try to provide visual support not only for the query specification process. but also for evaluating query results and. thereafter, refining the query accordingly. The main idea of our system is to represent as many data items as possible by the pixels of the display device. By arranging and coloring the pixels according to the relevance for the query, the user gets a visual impression of the resulting data set and of its relevance for the query. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. By using multiple windows for different parts of the query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. To support complex queries, we introduce the notion of approximate joins which allow the user to find data items that only approximately fulfill join conditions. We also present ideas how our technique may be extended to support the interoperation of heterogeneous databases. Finally, we discuss the performance problems that are caused by interfacing to existing database systems and present ideas to solve these problems by using data structures supporting a multidimensional search of the database
A comparison of two global optimization algorithms with sequential niche technique for structural model updating
Peer reviewedPostprin
Formalising the multidimensional nature of social networks
Individuals interact with conspecifics in a number of behavioural contexts or
dimensions. Here, we formalise this by considering a social network between n
individuals interacting in b behavioural dimensions as a nxnxb multidimensional
object. In addition, we propose that the topology of this object is driven by
individual needs to reduce uncertainty about the outcomes of interactions in
one or more dimension. The proposal grounds social network dynamics and
evolution in individual selection processes and allows us to define the
uncertainty of the social network as the joint entropy of its constituent
interaction networks. In support of these propositions we use simulations and
natural 'knock-outs' in a free-ranging baboon troop to show (i) that such an
object can display a small-world state and (ii) that, as predicted, changes in
interactions after social perturbations lead to a more certain social network,
in which the outcomes of interactions are easier for members to predict. This
new formalisation of social networks provides a framework within which to
predict network dynamics and evolution under the assumption that it is driven
by individuals seeking to reduce the uncertainty of their social environment.Comment: 16 pages, 4 figure
Complexity models in design
Complexity is a widely used term; it has many formal and informal meanings. Several formal models of complexity can be applied to designs and design processes. The aim of the paper is to examine the relation between complexity and design. This argument runs in two ways. First designing provides insights into how to respond to complex systems – how to manage, plan and control them. Second, the overwhelming complexity of many design projects lead us to examine how better understanding of complexity science can lead to improved designs and processes. This is the focus of this paper. We start with an outline of some observations on where complexity arises in design, followed by a brief discussion of the development of scientific and formal conceptions of complexity. We indicate how these can help in understanding design processes and improving designs
Multiple Uncertainties in Time-Variant Cosmological Particle Data
Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables
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