Abstract

Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this article, we present two privacy-preserving data aggregation schemes for additive aggregation functions, which can be extended to approximate MAX/MIN aggregation functions. The first scheme - Cluster-based Private Data Aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme-Slice-Mix-Agg Rega Te (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme (TAG), where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.

Original languageEnglish (US)
Article number6
JournalACM Transactions on Sensor Networks
Volume8
Issue number1
DOIs
StatePublished - Aug 1 2011

Fingerprint

Data privacy
Personal digital assistants
Agglomeration
Wireless sensor networks
Communication
Polynomials
Network protocols

Keywords

  • Algorithms
  • Security

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

PDA : Privacy-preserving data aggregation for information collection. / He, Wenbo; Liu, Xue; Viet, Hoang; Nahrstedt, Klara; Abdelzaher, Tarek.

In: ACM Transactions on Sensor Networks, Vol. 8, No. 1, 6, 01.08.2011.

Research output: Contribution to journalArticle

@article{b3a86e5a1d8347b3a01f4ecfd74694b2,
title = "PDA: Privacy-preserving data aggregation for information collection",
abstract = "Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this article, we present two privacy-preserving data aggregation schemes for additive aggregation functions, which can be extended to approximate MAX/MIN aggregation functions. The first scheme - Cluster-based Private Data Aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme-Slice-Mix-Agg Rega Te (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme (TAG), where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.",
keywords = "Algorithms, Security",
author = "Wenbo He and Xue Liu and Hoang Viet and Klara Nahrstedt and Tarek Abdelzaher",
year = "2011",
month = "8",
day = "1",
doi = "10.1145/1993042.1993048",
language = "English (US)",
volume = "8",
journal = "ACM Transactions on Sensor Networks",
issn = "1550-4859",
publisher = "Association for Computing Machinery (ACM)",
number = "1",

}

TY - JOUR

T1 - PDA

T2 - Privacy-preserving data aggregation for information collection

AU - He, Wenbo

AU - Liu, Xue

AU - Viet, Hoang

AU - Nahrstedt, Klara

AU - Abdelzaher, Tarek

PY - 2011/8/1

Y1 - 2011/8/1

N2 - Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this article, we present two privacy-preserving data aggregation schemes for additive aggregation functions, which can be extended to approximate MAX/MIN aggregation functions. The first scheme - Cluster-based Private Data Aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme-Slice-Mix-Agg Rega Te (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme (TAG), where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.

AB - Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this article, we present two privacy-preserving data aggregation schemes for additive aggregation functions, which can be extended to approximate MAX/MIN aggregation functions. The first scheme - Cluster-based Private Data Aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme-Slice-Mix-Agg Rega Te (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme (TAG), where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.

KW - Algorithms

KW - Security

UR - http://www.scopus.com/inward/record.url?scp=80052972721&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80052972721&partnerID=8YFLogxK

U2 - 10.1145/1993042.1993048

DO - 10.1145/1993042.1993048

M3 - Article

AN - SCOPUS:80052972721

VL - 8

JO - ACM Transactions on Sensor Networks

JF - ACM Transactions on Sensor Networks

SN - 1550-4859

IS - 1

M1 - 6

ER -