Campus Event Calendar
Event Entry
What and Who
Towards Use-Ability in Private Data Analytics
Reinhard Munz
MMCI
SWS Student Defense Talks - Thesis Proposal
SWS
Public Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Friday, 4 August 2017
13:30
60 Minutes
G26
111
Kaiserslautern
Abstract
Private data analytics systems preferably provide required analytic accuracy to analysts and specified privacy to individuals whose data is analyzed. Devising a general system that works for a broad range of datasets and analytic scenarios has proven to be difficult.
Despite the advent of differentially private systems with proven formal privacy guarantees, industry still uses their inferior ad-hoc predecessors that are able to provide better analytic accuracy. Differentially private mechanisms often need to add large amounts of noise to statistical results, which impairs their use-ability.
In my thesis I follow two approaches to improve use-ability of private data analytics systems in general and differentially private systems in particular. First, I revisit ad-hoc mechanisms and explore the possibilities of systems that do not provide Differential Privacy or only a weak version thereof. In my second approach I use the insights gained before to propose UniTraX, the first differentially private analytics system that allows to analyze part of a protected dataset without affecting other parts and without having to give up on guaranteed accuracy.
Despite the advent of differentially private systems with proven formal privacy guarantees, industry still uses their inferior ad-hoc predecessors that are able to provide better analytic accuracy. Differentially private mechanisms often need to add large amounts of noise to statistical results, which impairs their use-ability.
In my thesis I follow two approaches to improve use-ability of private data analytics systems in general and differentially private systems in particular. First, I revisit ad-hoc mechanisms and explore the possibilities of systems that do not provide Differential Privacy or only a weak version thereof. In my second approach I use the insights gained before to propose UniTraX, the first differentially private analytics system that allows to analyze part of a protected dataset without affecting other parts and without having to give up on guaranteed accuracy.
Contact
--email hidden
Video Broadcast
Yes
Saarbrücken
E1 5
105
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Maria-Louise Albrecht, 07/31/2017 16:05 -- Created document.