Confidentiality Protection for Distributed Sensor Data Aggregation

Abstract

Abstract-Efficiency and security are two basic requirements for sensor network design. However, these requirements could be sharply contrary to each other in some scenarios. For example, innetwork data aggregation can significantly reduce communication overhead and thus has been adopted widely as a means to improve network efficiency; however, the adoption of in-network data aggregation may prevent data from being encrypted since it is a prerequisite for aggregation that data be accessible during forwarding. In this paper, we address this dilemma by proposing a family of secret perturbation-based schemes that can protect sensor data confidentiality without disrupting additive data aggregation. Extensive simulations are also conducted to evaluate the proposed schemes. The results show that our schemes provide confidentiality protection for both raw and aggregated data items with an overhead lower than that of existing related schemes.

Privacy-Preserving in-Network Aggregation in Wireless Sensor Network

“…However, it is not a good candidate because in-network aggregation cannot be applied. There are some works about privacypreserving aggregation in sensor network have already be done (Girao et al, 2005;Castelluccia et al, 2005;He et al, 2007;Feng et al, 2008;Zhang et al, 2008;Westhoff et al, 2006;Roy et al, 2006;Shi et al, 2010). Due to space limitation, we only briefly review two works, PDA and PriSense, which are most close to our work.…”

Privacy and security protection in cloud integrated sensor networks

“…Our generic privacy preserving schemes are inspired by existing schemes in references [22,23,24,26]. In these schemes, each data item is encrypted using homomorphic encryption; hence, aggregation can be conducted on the ciphertext.…”

“…As mentioned above, designing a data aggregation scheme that can both preserve privacy and detect integrity attacks still remains as a challenging problem due to the apparent contradiction between the two goals. As in references [32,33], to test the integrity of aggregation usually needs the knowledge about the actual data being aggregated and results from the aggregation, which contradict with the requirement of preserving data privacy; meanwhile, as in references [25,26,27], to preserve the privacy of data before and after aggregation can be abused by malicious aggregators to modify the aggregation results without being detected.…”

“…Instead of using public key encryption, the symmetric additive homomorphic encryption is applied. Feng et al [26] pro-posed a series of schemes to optimize the communication overhead along this line of research. Later, He et al [25] proposed two privacy-preserving data aggregation schemes, namely, the cluster-based private data aggregation (CPDA) scheme and the slice-mix-aggregate (SMART) scheme, for additive aggregation functions, both of which can only tolerate up to a certain threshold number of compromised nodes.…”

“…Although the threshold can be raised by expanding the size of cluster for CPDA or increasing the number of slices for SMART, it will result in higher communication overhead. Moreover, one common limitation of existing schemes [22,23,24,25,26] is that they are only applicable for additive aggregation. It cannot perform other common aggregation functions such as max/min, percentile/median, and so on.…”