Probabilistic counting algorithms for data base applications
Journal of Computer and System Sciences
Simple proofs of occupancy tail bounds
Random Structures & Algorithms
An Optimal Algorithm for Monte Carlo Estimation
SIAM Journal on Computing
Resilient aggregation in sensor networks
Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks
Synopsis diffusion for robust aggregation in sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
An efficient scheme for authenticating public keys in sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
SDAP: a secure hop-by-Hop data aggregation protocol for sensor networks
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Attack-resilient hierarchical data aggregation in sensor networks
Proceedings of the fourth ACM workshop on Security of ad hoc and sensor networks
Secure hierarchical in-network aggregation in sensor networks
Proceedings of the 13th ACM conference on Computer and communications security
Containing denial-of-service attacks in broadcast authentication in sensor networks
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Catching elephants with mice: sparse sampling for monitoring sensor networks
Proceedings of the 5th international conference on Embedded networked sensor systems
Mitigating DoS attacks against broadcast authentication in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
An efficient integrity-preserving scheme for hierarchical sensor aggregation
WiSec '08 Proceedings of the first ACM conference on Wireless network security
Limiting DoS attacks during multihop data delivery in wireless sensor networks
International Journal of Security and Networks
SIA: Secure information aggregation in sensor networks
Journal of Computer Security - Special Issue on Security of Ad-hoc and Sensor Networks
Secure outsourced aggregation via one-way chains
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Efficient secure aggregation in sensor networks
HiPC'04 Proceedings of the 11th international conference on High Performance Computing
A survey of security issues in wireless sensor networks
IEEE Communications Surveys & Tutorials
Secure outsourced aggregation via one-way chains
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Efficient error estimating coding: feasibility and applications
IEEE/ACM Transactions on Networking (TON)
Secure Distributed Data Aggregation
Foundations and Trends in Databases
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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.