Title: Privacy-preserving Data Aggregation over Participatory Networks

Abstract:
The emerging participatory networked embedded systems, 
designed for aggregated information collection with fine-granularity, 
are viewed as new generation of pervasive computing systems.
 We expect both social and economic impact of participatory networks.
 Applications of participatory networks are likely to deal with 
highly sensitive or private information.
Hence, one of the pressing concerns of users is the confidentiality 
of the data collected about them. Therefore, we need a way to 
collect aggregated information while at the same time preserve data 
privacy.

In this talk, I will present two privacy-preserving data aggregation
 schemes for additive aggregation functions. The first scheme, 
Cluster-based Private Data Aggregation (CPDA), leverages clustering 
protocol and algebraic properties of polynomials. It has the 
advantage of incurring less communication overhead. The second 
scheme, Slice-Mix-AggRegaTe (SMART), builds on slicing techniques 
and the associative property of addition. It has the advantage of 
incurring less computation overhead. The goal of this work is to 
bridge the gap between collaborative data collection and privacy 
preservation of individual data. I assess the two schemes by 
privacy-preservation efficacy, communication overhead, and data 
aggregation accuracy. Since both schemes trade message overhead 
for privacy, I will propose efficiency enhancement method for 
privacy preserving data aggregation when message overhead is a 
big concern.