Spot-checkers

Ergün, Funda; Kannan, Sampath; Kumar, Saravana; Rubinfeld, Ronitt; Viswanathan, Mahesh

doi:10.1145/276698.276757

Cited by 59 publications

(52 citation statements)

References 29 publications

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“…To achieve this, we need two committed sequences, one sorted according to the measured values, and one sorted according to the sensor ids. We check that both sequences are sorted using Sort-Check-II spot checker from [11] with subsequent uniform sampling of pairs of neighboring elements (requiring O(log n/ε) and O(1/ε) samples, respectively; cf. Section 7.1).…”

Section: Our Approach For Medianmentioning

confidence: 99%

“…. m. A verifies the correctness of the counts by deriving from them the positions in the committed sorted sequence, and check the values in the committed sorted sequence are well sorted using the method Sort-Check-II spot checker from [11]. If also this check succeeds, B computes the average directly from the occurrence-counts, and compares it toā.…”

Section: Computing the Average By Counting Frequenciesmentioning

confidence: 99%

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SIA: Secure information aggregation in sensor networks

Chan

Perrig

Przydatek

et al. 2007

JCS

123

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Sensor networks promise viable solutions to many monitoring problems. However, the practical deployment of sensor networks faces many challenges imposed by real-world demands. Sensor nodes often have limited computation and communication resources and battery power. Moreover, in many applications sensors are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the sensor's cryptographic keys.One of the basic and indispensable functionalities of sensor networks is the ability to answer queries over the data acquired by the sensors. The resource constraints and security issues make designing mechanisms for information aggregation in large sensor networks particularly challenging.In this paper, we propose a novel framework for secure information aggregation in large sensor networks. In our framework certain nodes in the sensor network, called aggregators, help aggregating information requested by a query, which substantially reduces the communication overhead. By constructing efficient random sampling mechanisms and interactive proofs, we enable the user to verify that the answer given by the aggregator is a good approximation of the true value even when the aggregator and a fraction of the sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and the average of the measurements, for the estimation of the network size, and for finding the minimum and maximum sensor reading. Our protocols require only sublinear communication between the aggregator and the user. To the best of our knowledge, this paper is the first on secure information aggregation in sensor networks that can handle a malicious aggregator and sensor nodes. *

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Section: Our Approach For Medianmentioning

confidence: 99%

Section: Computing the Average By Counting Frequenciesmentioning

confidence: 99%

SIA: Secure information aggregation in sensor networks

Chan

Perrig

Przydatek

et al. 2007

JCS

123

View full text Add to dashboard Cite

show abstract

“…Furthermore, as shown in [19], obfuscating programs while preserving its functionality is theoretically impossible. Result checking [20] examines the validity of intermediate results produced by the program, but it is inappropriate for use in battery-powered devices because it continuously incurs verification overhead. Aucsmith [21] proposed to store the encrypted executable and decrypt it before execution.…”

Section: Tamper-proofing Techniquesmentioning

confidence: 99%

On Building a Lightweight Security Architecture for Sensor Networks

Park

Shin

2006

Emerging Directions in Embedded and Ubiquitous Computing

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Abstract. Sensor networks are characterized by their large-scale and unattended deployment that invites numerous critical attacks, thereby necessitating high-level security support for their intended applications and services. However, making sensor networks secure is challenging due mainly to the fact that sensors are battery-powered and it is usually very difficult to change or recharge their batteries. In this paper, we give a comprehensive overview of recent research results for securing such sensor networks, and then describe how to build a security framework, called a Lightweight Security Architecture (LiSA), for sensor networks, which achieves energy-aware security via closely-coupled, mutually-complementary security solutions.

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“…. , n} → R, after querying O(log n)/ε function values, we can, with probability at least 2/3, accept f if it is monotone and reject it if it is ε-far from being monotone [3]. These methods do not provide for tolerant property testing, however.…”

Section: Introductionmentioning

confidence: 99%

“…The starting point of our algorithm is the property tester of Ergun et al [3], which relies on a key fact: There exist at least ε f n "critical" integers i ∈ {1, . .…”

Section: Introductionmentioning

confidence: 99%

Estimating the distance to a monotone function

Ailon

Chazelle

Seshadhri

et al. 2007

Random Struct Algorithms

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Abstract. In standard property testing, the task is to distinguish between objects that have a property P and those that are ε-far from P, for some ε > 0. In this setting, it is perfectly acceptable for the tester to provide a negative answer for every input object that does not satisfy P. This implies that property testing in and of itself cannot be expected to yield any information whatsoever about the distance from the object to the property. We address this problem in this paper, restricting our attention to monotonicity testing.

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Spot-checkers

Cited by 59 publications

References 29 publications

SIA: Secure information aggregation in sensor networks

SIA: Secure information aggregation in sensor networks

On Building a Lightweight Security Architecture for Sensor Networks

Estimating the distance to a monotone function

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