10,478 research outputs found
Efficient Computations of a Security Index for False Data Attacks in Power Networks
The resilience of Supervisory Control and Data Acquisition (SCADA) systems
for electric power networks for certain cyber-attacks is considered. We analyze
the vulnerability of the measurement system to false data attack on
communicated measurements. The vulnerability analysis problem is shown to be
NP-hard, meaning that unless there is no polynomial time algorithm to
analyze the vulnerability of the system. Nevertheless, we identify situations,
such as the full measurement case, where it can be solved efficiently. In such
cases, we show indeed that the problem can be cast as a generalization of the
minimum cut problem involving costly nodes. We further show that it can be
reformulated as a standard minimum cut problem (without costly nodes) on a
modified graph of proportional size. An important consequence of this result is
that our approach provides the first exact efficient algorithm for the
vulnerability analysis problem under the full measurement assumption.
Furthermore, our approach also provides an efficient heuristic algorithm for
the general NP-hard problem. Our results are illustrated by numerical studies
on benchmark systems including the IEEE 118-bus system
Towards trajectory anonymization: a generalization-based approach
Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing
anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques
Fast multi-image matching via density-based clustering
We consider the problem of finding consistent matches
across multiple images. Previous state-of-the-art solutions
use constraints on cycles of matches together with convex
optimization, leading to computationally intensive iterative
algorithms. In this paper, we propose a clustering-based
formulation. We first rigorously show its equivalence with
the previous one, and then propose QuickMatch, a novel
algorithm that identifies multi-image matches from a density
function in feature space. We use the density to order the
points in a tree, and then extract the matches by breaking this
tree using feature distances and measures of distinctiveness.
Our algorithm outperforms previous state-of-the-art methods
(such as MatchALS) in accuracy, and it is significantly faster
(up to 62 times faster on some bechmarks), and can scale to
large datasets (with more than twenty thousands features).Accepted manuscriptSupporting documentatio
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