Anonymous Data Collection in Sensor Networks

Horey, James; Groat, Michael M.; Forrest, Stephanie; Esponda, Fernando

doi:10.1109/mobiq.2007.4451016

Cited by 65 publications

(34 citation statements)

References 20 publications

(16 reference statements)

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“…Horney et al [37] proposed a set of protocols that enable anonymous data collection in a sensor network. Sensor nodes, instead of transmitting their actual data, transmit a sample of the data complement to a base station, which then uses the negative samples to reconstruct a histogram of the original sensor readings.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Mobile Nodes for Preserving Node Privacy in Mobile Crowd Sensing

Chen

Zheng

Weng

2018

Wireless Communications and Mobile Computing

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Mobile crowd sensing has been a very important paradigm for collecting sensing data from a large number of mobile nodes dispersed over a wide area. Although it provides a powerful means for sensing data collection, mobile nodes are subject to privacy leakage risks since the sensing data from a mobile node may contain sensitive information about the sensor node such as physical locations. Therefore, it is essential for mobile crowd sensing to have a privacy preserving scheme to protect the privacy of mobile nodes. A number of approaches have been proposed for preserving node privacy in mobile crowd sensing. Many of the existing approaches manipulate the sensing data so that attackers could not obtain the privacy-sensitive data. The main drawback of these approaches is that the manipulated data have a lower utility in real-world applications. In this paper, we propose an approach called 3 to preserve the privacy of the mobile nodes in a mobile crowd sensing system, leveraging node mobility. In essence, a mobile node determines a routing path that consists of a sequence of intermediate mobile nodes and then forwards the sensing data along the routing path. By using asymmetric encryptions, it is ensured that a malicious node is not able to determine the source nodes by tracing back along the path. With our approach, upper-layer applications are able to access the original sensing data from mobile nodes, while the privacy of the mobile node is not compromised. Our theoretical analysis shows that the proposed approach achieves a high level of privacy preserving capability. The simulation results also show that the proposed approach incurs only modest overhead.

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Section: Related Workmentioning

confidence: 99%

Leveraging Mobile Nodes for Preserving Node Privacy in Mobile Crowd Sensing

Chen

Zheng

Weng

2018

Wireless Communications and Mobile Computing

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“…Multiple proposals have been developed, from the aggregation of values [5,6,7,28,29,30,31,32,33,34,35,36], to additions [2,3,26,37,38,39,40,41,42,43], calculus of minimums or maximums [10,44,45] or even more complex tasks such as averages [9,10,30,46], variances [9,46], range queries [6] or logic operations [8].…”

Section: Related Workmentioning

confidence: 99%

“…Anonymity is specially remarkable because aggregators should not learn particular data. Some proposals focus on the anonymity of messages passed through aggregator nodes [6,29,44] or on identity preservation of such nodes [7] or nodes providing information [2,3,5,26,38]. Anonymity is generally achieved by the use of cryptographic schemes, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Values are in plain text but the position of the real values in the array is only known by the node which is root of the tree. Likewise, Honrey et al [29] do not use cryptography but negative surveys. Negative values are transmitted from nodes providing information to the overall aggregator, which reconstructs the final histogram of data.…”

Section: Related Workmentioning

confidence: 99%

“…Privacy preservation against observers is considered in [2,3,5,7,8,29,26,44,31,41,42,46,36,25,48]. The use of cryptography is demanding to manage this issue [2,3,5,7,8,28,29,26,38,46,36,25,48], as well as randomized arrays or negative surveys [44].…”

Section: Related Workmentioning

confidence: 99%

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PAgIoT – Privacy-preserving Aggregation protocol for Internet of Things

González-Manzano

Fuentes

Pastrana

et al. 2016

Journal of Network and Computer Applications

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Modern society highly relies on the use of cyberspace to perform a huge variety of activities, such as social networking or e-commerce, and new technologies are continuously emerging. As such, computer systems may store a huge amount of information, which makes data analysis and storage a challenge. Information aggregation and correlation are two basic mechanisms to reduce the problem size, for example by filtering out redundant data or grouping similar one. These processes require high processing capabilities, and thus their application in Internet of Things (IoT) scenarios is not straightforward due to resource constraints. Furthermore, privacy issues may arise when the data at stake is personal. In this paper we propose PAgIoT, a Privacy-preserving Aggregation protocol suitable for IoT settings.It enables multi-attribute aggregation for groups of entities while allowing for privacy-preserving value correlation. Results show that PAgIoT is resis- * Corresponding author Email addresses: lgmanzan@inf.uc3m.es (L. González-Manzano a * ), jfuentes@inf.uc3m.es (José M. de Fuentes a ), spastran@inf.uc3m.es (Sergio Pastrana a ), pperis@inf.uc3m.es ( Pedro Peris-Lopez a ), luis@iec.csic.es (Luis Hernández-Encinas b )tant to security attacks, it outperforms existing proposals that provide with the same security features, and it is feasible in resource-constrained devices and for aggregation of up to 10 attributes in big networks.

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Negative Survey-Based Privacy Protection of Cloud Data

Liu

Tang

2015

Advances in Swarm and Computational Intelligence

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Anonymous Data Collection in Sensor Networks

Cited by 65 publications

References 20 publications

Leveraging Mobile Nodes for Preserving Node Privacy in Mobile Crowd Sensing

Leveraging Mobile Nodes for Preserving Node Privacy in Mobile Crowd Sensing

PAgIoT – Privacy-preserving Aggregation protocol for Internet of Things

Negative Survey-Based Privacy Protection of Cloud Data

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