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Efficient aggregation of encrypted data in wireless sensor networks

Castelluccia, C., Mykletun, E., Tsudik, G.·2005·The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services

Abstract

Wireless sensor networks (WSNs) are ad-hoc networks composed of tiny devices with limited computation and energy capacities. For such devices, data transmission is a very energy-consuming operation. It thus becomes essential to the lifetime of a WSN to minimize the number of bits sent by each device. One well-known approach is to aggregate sensor data (e.g., by adding) along the path from sensors to the sink. Aggregation becomes especially challenging if end-to-end privacy between sensors and the sink is required. In this paper, we propose a simple and provably secure additively homomorphic stream cipher that allows efficient aggregation of encrypted data. The new cipher only uses modular additions (with very small moduli) and is therefore very well suited for CPU-constrained devices. We show that aggregation based on this cipher can be used to efficiently compute statistical values such as mean, variance and standard deviation of sensed data, while achieving significant bandwidth gain.

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Metadata

Authors
Castelluccia, C., Mykletun, E., & Tsudik, G.
Venue
The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
Publisher
IEEE
Published
2005
Pages
109-117
OpenAlex
W2102832611
Cited by
525
References
1
FWCI
11.93
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Efficient aggregation of encrypted data in wireless sensor networks. https://app.cochat.ai/papers/10.1109/mobiquitous.2005.25
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Castelluccia, C., Mykletun, E., & Tsudik, G. (2005). Efficient aggregation of encrypted data in wireless sensor networks. The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services. https://doi.org/10.1109/mobiquitous.2005.25

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.