37,473 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
Hypersensitivity to Perturbations in the Quantum Baker's Map
We analyze a randomly perturbed quantum version of the baker's
transformation, a prototype of an area-conserving chaotic map. By numerically
simulating the perturbed evolution, we estimate the information needed to
follow a perturbed Hilbert-space vector in time. We find that the Landauer
erasure cost associated with this information grows very rapidly and becomes
much larger than the maximum statistical entropy given by the logarithm of the
dimension of Hilbert space. The quantum baker's map thus displays a
hypersensitivity to perturbations that is analogous to behavior found earlier
in the classical case. This hypersensitivity characterizes ``quantum chaos'' in
a way that is directly relevant to statistical physics.Comment: 8 pages, LATEX, 3 Postscript figures appended as uuencoded fil
On the Global Convergence of Continuous-Time Stochastic Heavy-Ball Method for Nonconvex Optimization
We study the convergence behavior of the stochastic heavy-ball method with a
small stepsize. Under a change of time scale, we approximate the discrete
method by a stochastic differential equation that models small random
perturbations of a coupled system of nonlinear oscillators. We rigorously show
that the perturbed system converges to a local minimum in a logarithmic time.
This indicates that for the diffusion process that approximates the stochastic
heavy-ball method, it takes (up to a logarithmic factor) only a linear time of
the square root of the inverse stepsize to escape from all saddle points. This
results may suggest a fast convergence of its discrete-time counterpart. Our
theoretical results are validated by numerical experiments.Comment: accepted at IEEE International Conference on Big Data in 201
Reconstruction of dielectric constants of multi-layered optical fibers using propagation constants measurements
We present new method for the numerical reconstruction of the variable
refractive index of multi-layered circular weakly guiding dielectric waveguides
using the measurements of the propagation constants of their eigenwaves. Our
numerical examples show stable reconstruction of the dielectric permittivity
function for random noise level using these measurements
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