29,702 research outputs found
Privacy-Preserving Shortest Path Computation
Navigation is one of the most popular cloud computing services. But in
virtually all cloud-based navigation systems, the client must reveal her
location and destination to the cloud service provider in order to learn the
fastest route. In this work, we present a cryptographic protocol for navigation
on city streets that provides privacy for both the client's location and the
service provider's routing data. Our key ingredient is a novel method for
compressing the next-hop routing matrices in networks such as city street maps.
Applying our compression method to the map of Los Angeles, for example, we
achieve over tenfold reduction in the representation size. In conjunction with
other cryptographic techniques, this compressed representation results in an
efficient protocol suitable for fully-private real-time navigation on city
streets. We demonstrate the practicality of our protocol by benchmarking it on
real street map data for major cities such as San Francisco and Washington,
D.C.Comment: Extended version of NDSS 2016 pape
Probabilistic Relational Model Benchmark Generation
The validation of any database mining methodology goes through an evaluation
process where benchmarks availability is essential. In this paper, we aim to
randomly generate relational database benchmarks that allow to check
probabilistic dependencies among the attributes. We are particularly interested
in Probabilistic Relational Models (PRMs), which extend Bayesian Networks (BNs)
to a relational data mining context and enable effective and robust reasoning
over relational data. Even though a panoply of works have focused, separately ,
on the generation of random Bayesian networks and relational databases, no work
has been identified for PRMs on that track. This paper provides an algorithmic
approach for generating random PRMs from scratch to fill this gap. The proposed
method allows to generate PRMs as well as synthetic relational data from a
randomly generated relational schema and a random set of probabilistic
dependencies. This can be of interest not only for machine learning researchers
to evaluate their proposals in a common framework, but also for databases
designers to evaluate the effectiveness of the components of a database
management system
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