15,832 research outputs found
The calibration of the Sudbury Neutrino Observatory using uniformly distributed radioactive sources
The production and analysis of distributed sources of 24Na and 222Rn in the
Sudbury Neutrino Observatory (SNO) are described. These unique sources provided
accurate calibrations of the response to neutrons, produced through
photodisintegration of the deuterons in the heavy water target, and to low
energy betas and gammas. The application of these sources in determining the
neutron detection efficiency and response of the 3He proportional counter
array, and the characteristics of background Cherenkov light from trace amounts
of natural radioactivity is described.Comment: 24 pages, 13 figure
Mining Frequent Graph Patterns with Differential Privacy
Discovering frequent graph patterns in a graph database offers valuable
information in a variety of applications. However, if the graph dataset
contains sensitive data of individuals such as mobile phone-call graphs and
web-click graphs, releasing discovered frequent patterns may present a threat
to the privacy of individuals. {\em Differential privacy} has recently emerged
as the {\em de facto} standard for private data analysis due to its provable
privacy guarantee. In this paper we propose the first differentially private
algorithm for mining frequent graph patterns.
We first show that previous techniques on differentially private discovery of
frequent {\em itemsets} cannot apply in mining frequent graph patterns due to
the inherent complexity of handling structural information in graphs. We then
address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling
based algorithm. Unlike previous work on frequent itemset mining, our
techniques do not rely on the output of a non-private mining algorithm.
Instead, we observe that both frequent graph pattern mining and the guarantee
of differential privacy can be unified into an MCMC sampling framework. In
addition, we establish the privacy and utility guarantee of our algorithm and
propose an efficient neighboring pattern counting technique as well.
Experimental results show that the proposed algorithm is able to output
frequent patterns with good precision
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