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

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 resul...

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Vydané v:2009 International Conference on Information Processing in Sensor Networks s. 1 - 12
Hlavný autor: Haifeng Yu
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Washington, DC, USA IEEE Computer Society 13.04.2009
IEEE
Edícia:ACM Conferences
Predmet:
Aggregates
Base stations
Computer security
Computer systems organization > Architectures > Distributed architectures
Information systems > Data management systems > Database management system engines > Database query processing
Information systems > Data management systems > Database management system engines > Parallel and distributed DBMSs
Interference
Large-scale systems
Mathematics of computing > Probability and statistics > Statistical paradigms
Mathematics of computing > Probability and statistics > Statistical paradigms > Statistical graphics
Networks > Network types > Mobile networks
Networks > Network types > Wireless access networks
Protocols
Sampling methods
Security and privacy > Database and storage security
Sensor phenomena and characterization
Sensor systems
Theory of computation > Theory and algorithms for application domains > Database theory > Database query processing and optimization (theory)
Theory of computation > Theory and algorithms for application domains > Database theory > Theory of database privacy and security
Wireless sensor networks
ISBN:1424451086, 9781424451081
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Popis
Shrnutí: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.
ISBN:1424451086
9781424451081
DOI:10.5555/1602165.1602168