Refreshments 3:20 p.m.
Abstract: Mobile devices today are equipped with a range of sensors
that can be effectively used to infer a user's context including her
location, transportation mode and social state. This has empowered
personalized applications such as the targeting of advertisements and
the monitoring of physical and mental health. However, these
context-aware mobile applications raise serious privacy concerns.
People already believe that risks of sharing location information
outweigh benefits in many location-based services.
In this talk, I will present scalable techniques to find more
attractive tradeoffs of privacy and utility from personalization.
I will focus on targeted advertising as an application. Targeted
advertising requires statistics over sensitive context and click data.
To collect these large amounts of statistics, I will present an
efficient distributed algorithm that preserves privacy. I will discuss
two scalability challenges: (1) The number of users is large and the
set of users is changing constantly. This makes it difficult to
execute a distributed protocol.
(2) The domain over which we collect click statistics can be huge.
Thus, our protocol must scale sub-linearly in the domain size.
These statistics are then used by our ad serving platform to
efficiently deliver targeted advertisements to users based on the
amount of personal information they were willing to share.
Mila Hardt works on search quality at Twitter. She received her
PhD at Cornell University advised by Johannes Gehrke in 2012. Her
research interests lie in the area of data management with focus on
privacy. She is the recipient of a Microsoft Research Women
Scholarship and a Google Engineering Intern Scholarship.