Efficient Secure Aggregation in Sensor Networks

Jadia, Pawan; Mathuria, Anish

doi:10.1007/978-3-540-30474-6_10

Cited by 38 publications

(38 citation statements)

References 10 publications

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“…Although some aggregation protocols [5,6] are (optimally) secure against a single corrupted node without corruption of intermediate nodes, these protocols are not optimally secure against multiple corrupted nodes. Some aggregation protocols [1,2,20] are optimally secure against multiple corrupted nodes even if intermediate nodes are corrupted.…”

Section: Related Workmentioning

confidence: 99%

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Efficient and Optimally Secure In-Network Aggregation in Wireless Sensor Networks

Miyaji

Omote

2011

Information Security Applications

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Abstract. In many wireless sensor network applications, the data collection sink (base station) needs to find the aggregated statistics of the network. Readings from sensor nodes are aggregated at intermediate nodes to reduce the communication cost. However, the previous optimally secure in-network aggregation protocols against multiple corrupted nodes require two round-trip communications between each node and the base station, including the result-checking phase whose congestion is O(log n) where n is the total number of sensor nodes. In this paper 1 , we propose an efficient and optimally secure sensor network aggregation protocol against multiple corrupted nodes by a weak adversary. Our protocol achieves one round-trip communication to satisfy optimal security without the result-checking phase, by conducting aggregation along with the verification, based on the idea of TESLA technique. Furthermore, we show that the congestion is constant. This means that our protocol suits large-scale wireless sensor networks.

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Section: Related Workmentioning

confidence: 99%

“…Readings from sensor nodes are aggregated at intermediate nodes to reduce the communication cost. This process is called in-network aggregation [1][2][3][4][5][6][18][19][20]. Since aggregation reduces the amount of data to be transmitted through the network, it consequently decreases bandwidth consumption and energy depletion.…”

Section: Introductionmentioning

confidence: 99%

Efficient and Optimally Secure In-Network Aggregation in Wireless Sensor Networks

Miyaji

Omote

2011

Information Security Applications

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“…Although some aggregation protocols [6], [7] are (optimally) secure against a single corrupted node without corruption of intermediate nodes, these protocols are not optimally secure against multiple corrupted nodes. Some aggregation protocols [2], [3], [21] are optimally secure against multiple corrupted nodes even if intermediate nodes are corrupted.…”

Section: Related Workmentioning

confidence: 99%

“…1. This process is called innetwork aggregation [2]- [7], [19]- [21]. Since aggregation reduces the amount of data to be transmitted through the network, it consequently decreases bandwidth consumption and energy depletion.…”

Section: Introductionmentioning

confidence: 99%

Efficient and Secure Aggregation of Sensor Data against Multiple Corrupted Nodes

Miyaji

Omote

2011

IEICE Trans. Inf. & Syst.

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SUMMARYWireless Sensor Networks (WSNs) rely on in-network aggregation for efficiency, that is, readings from sensor nodes are aggregated at intermediate nodes to reduce the communication cost. However, the previous optimally secure in-network aggregation protocols against multiple corrupted nodes require two round-trip communications between each node and the base station, including the result-checking phase whose congestion is O(log n) where n is the total number of sensor nodes. In this paper * * , we propose an efficient and optimally secure sensor network aggregation protocol against multiple corrupted nodes by a random-walk adversary. Our protocol achieves one round-trip communication to satisfy optimal security without the result-checking phase, by conducting aggregation along with the verification, based on the idea of TESLA technique. Furthermore, we show that the congestion complexity, communication complexity and computational cost in our protocol are constant, i.e., O(1).

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“…The second attack can be much more serious since even a single malicious sensor can completely corrupt the final result. Thus the need to make aggregation queries secure against the second attack has been widely acknowledged [1,2,6,10,14,15,18,26,27].…”

Section: Introductionmentioning

confidence: 99%

Secure and highly-available aggregation queries in large-scale sensor networks via set sampling

2011

Distrib. Comput.

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Wireless sensor networks are often queried for aggregates such as predicate count, sum, and average. In untrusted environments, sensors may potentially be compromised. Existing approaches for securely answering aggregation queries in untrusted sensor networks can detect whether the aggregation result is corrupted by an attacker. However, the attacker (controlling the compromised sensors) can keep corrupting the result, rendering the system unavailable.This paper aims to enable aggregation queries to tolerate instead of just detecting the adversary. To this end, we propose a novel tree sampling algorithm that directly uses sampling to answer aggregation queries. It leverages a novel set sampling technique to overcome a key and well-known obstacle in sampling -traditional sampling technique is only effective when the predicate count or sum is large. Set sampling can efficiently sample a set of sensors together, and determine whether any sensor in the set satisfies the predicate (but not how many). With set sampling as a building block, tree sampling can provably generate a correct answer despite adversarial interference, while without the drawbacks of traditional sampling techniques.

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Efficient Secure Aggregation in Sensor Networks

Cited by 38 publications

References 10 publications

Efficient and Optimally Secure In-Network Aggregation in Wireless Sensor Networks

Efficient and Optimally Secure In-Network Aggregation in Wireless Sensor Networks

Efficient and Secure Aggregation of Sensor Data against Multiple Corrupted Nodes

Secure and highly-available aggregation queries in large-scale sensor networks via set sampling

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