Abstract: |
When deploying sensor networks in environments that monitor people (e.g., monitoring water usage), both privacy and integrity are important. Several solutions have been proposed for privacy \cite{Castelluccia05}, \cite{Wenbo07}, and integrity \cite{Yang06}, \cite{Przydatek03}, \cite{Hu03}, \cite{Chan06}, \cite{Frikken08}. Unfortunately, these mechanisms are not easily composable. In this paper, we extend the splitting schemes proposed in \cite{Wenbo07} to provide privacy and integrity when computing the SUM aggregate.
Our scheme provides privacy even if the base station colludes with some
cluster heads, and provides integrity by detecting when individual nodes inflate or deflate their values too much. Our main contributions are: i) a new integrity measure
that is a relaxation of the one in \cite{Chan06}, ii) a new privacy measure called $k$-similarity, iii) a construction that satisfies both of these measures
for the computation of the SUM aggregate that avoids the usage of expensive cryptography,
and iv) experimental results that demonstrate the effectiveness of our techniques.
%For example, to provide privacy many prior schemes aggregate encrypted values, but this encryption prevents sanity checks, which are required by many of the previously proposed integrity mechanisms. The only work that addresses both privacy and integrity is \cite{Robert09}, which utilized monitoring nodes. This scheme
%assumes that these monitoring sensors are honest, and if they become compromised the scheme does not achieve privacy or integrity. Further, this scheme does not prevent corrupted sensor nodes from corrupting the final aggregate result. |