Efficient privacy-preserving stream aggregation in mobile sensing with low aggregation error

Qinghua Li, Guohong Cao

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

33 Scopus citations

Abstract

Aggregate statistics computed from time-series data contributed by individual mobile nodes can be very useful for many mobile sensing applications. Since the data from individual node may be privacy-sensitive, the aggregator should only learn the desired statistics without compromising the privacy of each node. To provide strong privacy guarantee, existing approaches add noise to each node's data and allow the aggregator to get a noisy sum aggregate. However, these approaches either have high computation cost, high communication overhead when nodes join and leave, or accumulate a large noise in the sum aggregate which means high aggregation error. In this paper, we propose a scheme for privacy-preserving aggregation of time-series data in presence of untrusted aggregator, which provides differential privacy for the sum aggregate. It leverages a novel ring-based interleaved grouping technique to efficiently deal with dynamic joins and leaves and achieve low aggregation error. Specifically, when a node joins or leaves, only a small number of nodes need to update their cryptographic keys. Also, the nodes only collectively add a small noise to the sum to ensure differential privacy, which is O(1) with respect to the number of nodes. Based on symmetric-key cryptography, our scheme is very efficient in computation.

Original languageEnglish (US)
Title of host publicationPrivacy Enhancing Technologies - 13th International Symposium, PETS 2013, Proceedings
Pages60-81
Number of pages22
DOIs
StatePublished - Oct 8 2013
Event13th International Symposium on Privacy Enhancing Technologies, PETS 2013 - Bloomington, IN, United States
Duration: Jul 10 2013Jul 12 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7981 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Symposium on Privacy Enhancing Technologies, PETS 2013
CountryUnited States
CityBloomington, IN
Period7/10/137/12/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Efficient privacy-preserving stream aggregation in mobile sensing with low aggregation error'. Together they form a unique fingerprint.

  • Cite this

    Li, Q., & Cao, G. (2013). Efficient privacy-preserving stream aggregation in mobile sensing with low aggregation error. In Privacy Enhancing Technologies - 13th International Symposium, PETS 2013, Proceedings (pp. 60-81). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7981 LNCS). https://doi.org/10.1007/978-3-642-39077-7_4