12 research outputs found
Privacy-preserving sharing of sensitive semantic locations under road-network constraints
No full textThis paper presents a privacy-preserving framework
for the protection of sensitive positions in real time trajectories.
We assume a scenario in which the sensitivity of user’s positions
is space-varying, and so depends on the spatial context, while
the user’s movement is confined to road networks and places.
Typical users are the non-anonymous members of a geo-social
network who agree to share their exact position whenever such
position does not fall within a sensitive place, e.g. a hospital.
Suspending location sharing while the user is inside a sensitive
place is not an appropriate solution because the user’s stopovers
can be easily inferred from the user’s trace. In this paper we
present an extension of the semantic location cloaking model
originally developed for the cloaking of non-correlated positions
in an unconstrained space. We investigate different algorithms
for the generation of cloaked regions over the graph representing
the urban setting. We also integrate methods to prevent velocity based
linkage attacks. Finally we evaluate experimentally the
algorithms using a real data set
SAWLnet: Sensitivity AWare Location cloaking on road-NETworks
No full textAbstract—Location based queries are increasingly common
in mobile applications, and the associated privacy issues have
become a hot research topic in the last years. Most of the
current approaches, however, do not account for the location of
potentially sensitive places and for constraints on the movement
of users, such as speed limits or network contraints.
In this demo we present different deployment scenarios of
a privacy-preserving framework for the protection of sensitive
positions in real time trajectories. We assume that the sensitivity
of users’ positions depends on the spatial context, while the users’
movement is confined to road networks and places. Further, the
users are non-anonymous, as in the case of geo-social network
members who agree to share their exact position whenever it
does not fall within a sensitive place, e.g. a hospital.
We will show that our proposal is suitable for different classes
of devices and can be integrated in different kind of location
based applications
Comparison of dose accuracy between 2D array detectors and Epid for IMRT of nasopharynx cancer
No full text
Road Network-Aware Spatial Alarms
No full textRoad network-aware spatial alarms extend the concept of time-based alarms to spatial dimension and remind us when we travel on spatially constrained road networks and enter some predefined locations of interest in the future. This paper argues that road network-aware spatial alarms need to be processed by taking into account spatial constraints on road networks and mobility patterns of mobile subscribers. We show that the Euclidian distance-based spatial alarm processing techniques tend to incur high client energy consumption due to unnecessarily frequent client wakeups. We design and develop a road network-aware spatial alarm processing system, called ROADALARM, with three unique features. First, we introduce the concept of road network-based spatial alarms using road network distance measures. Instead of using a rectangular region, a road network-aware spatial alarm is a star-like subgraph with an alarm target as the center of the star and border points as the scope of the alarm region. Second, we describe a baseline approach for spatial alarm processing by exploiting two types of filters. We use subscription filter and Euclidean lower bound filter to reduce the amount of shortest path computations required in both computing alarm hibernation time and performing alarm checks at the server. Last but not the least, we develop a suite of optimization techniques using motion-aware filters, which enable us to further increase the hibernation time of mobile clients and reduce the frequency of wakeups and alarm checks, while ensuring high accuracy of spatial alarm processing. Our experimental results show that the road network-aware spatial alarm processing significantly outperforms existing Euclidean space-based approaches, in terms of both the number of wakeups and the hibernation time at mobile clients and the number of alarm checks at the serve
Clustering Service Networks with Entity, Attribute, and Link Heterogeneity
No full textMany popular web service networks are content-rich in terms of heterogeneous types of entities and links, associated with incomplete attributes. Clustering such heterogeneous service networks demands new clustering techniques that can handle two heterogeneity challenges: (1) multiple types of entities co-exist in the same service network with multiple attributes, and (2) links between entities have diverse types and carry different semantics. Existing heterogeneous graph clustering techniques tend to pick initial centroids uniformly at random, specify the number k of clusters in advance, and fix k during the clustering process. In this paper, we propose Service Cluster, a novel heterogeneous service network clustering algorithm with four unique features. First, we incorporate various types of entity, attribute and link information into a unified distance measure. Second, we design a Discrete Steepest Descent method to naturally produce initial k and initial centroids simultaneously. Third, we propose a dynamic learning method to automatically adjust the link weights towards clustering convergence. Fourth, we develop an effective optimization strategy to identify new suitable k and k well-chosen centroids at each clustering iteration. Extensive evaluation on real datasets demonstrates that Service Cluster outperforms existing representative methods in terms of both effectiveness and efficiency
Scaling Iterative Graph Computations with GraphMap
No full textIn recent years, systems researchers have devoted consider-able effort to the study of large-scale graph processing. Ex-isting distributed graph processing systems such as Pregel, based solely on distributed memory for their computations, fail to provide seamless scalability when the graph data and their intermediate computational results no longer fit into the memory; and most distributed approaches for itera-tive graph computations do not consider utilizing secondary storage a viable solution. This paper presents GraphMap, a distributed iterative graph computation framework that maximizes access locality and speeds up distributed itera-tive graph computations by effectively utilizing secondary storage. GraphMap has three salient features: (1) It distin-guishes data states that are mutable during iterative compu-tations from those that are read-only in all iterations to max-imize sequential access and minimize random access. (2) It entails a two-level graph partitioning algorithm that enables balanced workloads and locality-optimized data placement. (3) It contains a proposed suite of locality-based optimiza-tions that improve computational efficiency. Extensive ex-periments on several real-world graphs show that GraphMap outperforms existing distributed memory-based systems for various iterative graph algorithms
