63 research outputs found
Security and privacy aspects of mobile applications for post-surgical care
Mobile technologies have the potential to improve patient monitoring, medical decision making and in general the efficiency and quality of health delivery. They also pose new security and privacy challenges. The objectives of this work are to (i) Explore and define security and privacy requirements on the example of a post-surgical care application, and (ii) Develop and test a pilot implementation Post-Surgical Care Studies of surgical out- comes indicate that timely treatment of the most common complications in compliance with established post-surgical regiments greatly improve success rates. The goal of our pilot application is to enable physician to optimally synthesize and apply patient directed best medical practices to prevent post-operative complications in an individualized patient/procedure specific fashion. We propose a framework for a secure protocol to enable doctors to check most common complications for their patient during in-hospital post- surgical care. We also implemented our construction and cryptographic protocols as an iPhone application on the iOS using existing cryptographic services and libraries
Differential Privacy in Metric Spaces: Numerical, Categorical and Functional Data Under the One Roof
We study Differential Privacy in the abstract setting of Probability on
metric spaces. Numerical, categorical and functional data can be handled in a
uniform manner in this setting. We demonstrate how mechanisms based on data
sanitisation and those that rely on adding noise to query responses fit within
this framework. We prove that once the sanitisation is differentially private,
then so is the query response for any query. We show how to construct
sanitisations for high-dimensional databases using simple 1-dimensional
mechanisms. We also provide lower bounds on the expected error for
differentially private sanitisations in the general metric space setting.
Finally, we consider the question of sufficient sets for differential privacy
and show that for relaxed differential privacy, any algebra generating the
Borel -algebra is a sufficient set for relaxed differential privacy.Comment: 18 Page
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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