Confidentiality and integrity for SUM aggregation in sensor networks

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 (Castelluccia et al., 2005), (He et al., 2007), and integrity (Yang et al., 2006), (Przydatek et al., 2003), (Hu and Evans, 2003), (Chan et al., 2006), (Frikken and Dougherty, 2008). Unfortunately, these mechanisms are not easily composable. In this paper, we extend the splitting schemes proposed in (He et al., 2007) 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 (Chan et al., 2006), 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.