KIPDA: K-indistinguishable privacy-preserving data aggregation in wireless sensor networks

Michael M. Groat, Wenbo Hey, Stephanie Forrest

Research output: Chapter in Book/Report/Conference proceedingConference contribution

135 Scopus citations


When wireless sensor networks accumulate sensitive or confidential data, privacy becomes an important concern. Sensors are often resource-limited and power-constrained, and data aggregation is commonly used to address these issues. However, providing privacy without disrupting in-network data aggregation is challenging. Although privacy-preserving data aggregation for additive and multiplicative aggregation functions has been studied, nonlinear aggregation functions such as maximum and minimum have not been well addressed. We present KIPDA, a privacy-preserving aggregation method, which we specialize for maximum and minimum aggregation functions. KIPDA obfuscates sensitive measurements by hiding them among a set of camouflage values, enabling k-indistinguishability for data aggregation. In principle, KIPDA can be used to hide a wide range of aggregation functions, although this paper considers only maximum and minimum. Because the sensitive data are not encrypted, it is easily and efficiently aggregated with minimal in-network processing delay. We quantify the efficiency of KIPDA in terms of power consumption and time delay, studying tradeoffs between the protocol's effectiveness and its resilience against collusion.

Original languageEnglish (US)
Title of host publication2011 Proceedings IEEE INFOCOM
Number of pages9
StatePublished - 2011
Externally publishedYes
EventIEEE INFOCOM 2011 - Shanghai, China
Duration: Apr 10 2011Apr 15 2011

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X



ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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