PDA: Privacy-preserving data aggregation for information collection

Wenbo He, Xue Liu, Hoang Viet, Klara Nahrstedt, Tarek Abdelzaher

Research output: Contribution to journalArticlepeer-review


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
Issue number1
StatePublished - Aug 2011


  • Algorithms
  • Security

ASJC Scopus subject areas

  • Computer Networks and Communications


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