Efficient and Robust Secure Aggregation for Sensor Networks

TL;DR

This paper introduces a secure aggregation system for sensor networks that balances robustness and efficiency by localizing and excluding malicious nodes only when manipulation is detected, ensuring reliable data collection.

Contribution

It presents a novel scheme that combines secure detection of faults with efficient aggregation, improving robustness without sacrificing performance.

Findings

The system effectively detects and excludes malicious nodes.

It achieves near in-network aggregation efficiency after fault removal.

The approach enhances data availability and security in sensor networks.

Abstract

Wireless Sensor Networks (WSNs) rely on in-network aggregation for efficiency, however, this comes at a price: A single adversary can severely influence the outcome by contributing an arbitrary partial aggregate value. Secure in-network aggregation can detect such manipulation. But as long as such faults persist, no aggregation result can be obtained. In contrast, the collection of individual sensor node values is robust and solves the problem of availability, yet in an inefficient way. Our work seeks to bridge this gap in secure data collection: We propose a system that enhances availability with an efficiency close to that of in-network aggregation. To achieve this, our scheme relies on costly operations to localize and exclude nodes that manipulate the aggregation, but \emph{only} when a failure is detected. The detection of aggregation disruptions and the removal of faulty nodes provides robustness. At the same time, after removing faulty nodes, the WSN can enjoy low cost (secure) aggregation. Thus, the high exclusion cost is amortized, and efficiency increases.