Campus Event Calendar
Event Entry
What and Who
Towards Usability in Private Data Analytics
Reinhard Munz
MMCI
SWS Student Defense Talks - Thesis Defense
SWS
Public Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Friday, 6 December 2019
14:00
60 Minutes
G26
607
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.
Differentially private systems allow for proven formal privacy guarantees. To provide these guarantees, however, differentially private mechanisms often need to add large amounts of noise to statistical results, which impairs their usability. Thus, despite the advent of mechanisms with formal guarantees, industry still uses inferior ad-hoc mechanisms that provide better analytic accuracy.
In my thesis I follow two approaches to improve the usability 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. Based on an attack analysis, I devise a set of new protection mechanisms including Query Based Bookkeeping (QBB). In contrast to previous systems, QBB only requires the history of analysts’ queries in order to provide privacy protection, but does NOT require any knowledge about the protected individuals’ data.
In my second approach I use the insights gained with QBB to propose UniTraX, the first differentially private analytics system that allows analysts to analyze part of a protected dataset without affecting the other parts and without giving up on accuracy. I show UniTraX’s usability by way of multiple case studies on real-world datasets across different domains. In all cases I demonstrate that UniTraX allows more queries than previous differentially private data analytics systems at moderate runtime overheads.
Differentially private systems allow for proven formal privacy guarantees. To provide these guarantees, however, differentially private mechanisms often need to add large amounts of noise to statistical results, which impairs their usability. Thus, despite the advent of mechanisms with formal guarantees, industry still uses inferior ad-hoc mechanisms that provide better analytic accuracy.
In my thesis I follow two approaches to improve the usability 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. Based on an attack analysis, I devise a set of new protection mechanisms including Query Based Bookkeeping (QBB). In contrast to previous systems, QBB only requires the history of analysts’ queries in order to provide privacy protection, but does NOT require any knowledge about the protected individuals’ data.
In my second approach I use the insights gained with QBB to propose UniTraX, the first differentially private analytics system that allows analysts to analyze part of a protected dataset without affecting the other parts and without giving up on accuracy. I show UniTraX’s usability by way of multiple case studies on real-world datasets across different domains. In all cases I demonstrate that UniTraX allows more queries than previous differentially private data analytics systems at moderate runtime overheads.
Contact
--email hidden
Video Broadcast
Yes
Saarbrücken
E1 5
005
SWS Space 2 (6312)
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logged in users only
Maria-Louise Albrecht, 12/03/2019 11:02 -- Created document.