The weekly SILO Seminar Series is made possible through the generous support of the 3M Company and its Advanced Technology Group


with additional support from the Analytics Group of the Northwestern Mutual Life Insurance Company

Northwestern Mutual

Privacy-assurances in multiple data-aggregation transactions

Parmesh Ramanathan, ECE Professor at Univ. Wisconsin

Date and Time: Mar 12, 2013 (12:30 PM)
Location: Orchard room (3280) at the Wisconsin Institute for Discovery Building


Aggregating data from a large number of users is integral part of the business
model of many companies. As a result, people are increasingly concerned
about privacy of their data. In this talk, I will present a few privacy-preserving
algorithms for aggregating data from a large number of users at
a third-party application. We consider aggregation through two commonly
used functions, weighted sum and maximum. Unlike traditional
secure multi-party computations, our algorithms are designed for scenarios
where the users do not know each other and they are not eager to exchange
information with each other. In fact, a key aspect of these algorithms is
that they do not require any direct communication between two users.
Another important aspect of these algorithms is that the privacy assurances
are provided even when the users are involved in multiple transactions with the
same third-party application. The second aspect is a major issue that
has not been widely addressed in literature. Finally, the algorithms
do not impose significant computational and communication burden on any user.
In particular, the computational and communication complexity required of an user
does not grow with number of users.