Load-balanced query dissemination in privacy-aware online communities

  • Authors:

  • Emiran Curtmola;Alin Deutsch;K. K. Ramakrishnan;Divesh Srivastava

  • Affiliations:

  • University of California - San Diego, San Diego, CA, USA;University of California - San Diego, San Diego, CA, USA;AT&T Labs Research, Florham Park, NJ, USA;AT&T Labs Research, Florham Park, NJ, USA

  • Venue:
  • Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
  • Year:
  • 2010

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Abstract

We propose a novel privacy-preserving distributed infrastructure in which data resides only with the publishers owning it. The infrastructure disseminates user queries to publishers, who answer them at their own discretion. The infrastructure enforces a publisher k-anonymity guarantee, which prevents leakage of information about which publishers are capable of answering a certain query. Given the virtual nature of the global data collection, we study the challenging problem of efficiently locating publishers in the community that contain data items matching a specified query. We propose a distributed index structure, UQDT, that is organized as a union of Query Dissemination Trees (QDTs), and realized on an overlay (i.e., logical) network infrastructure. Each QDT has data publishers as its leaf nodes, and overlay network nodes as its internal nodes; each internal node routes queries to publishers, based on a summary of the data advertised by publishers in its subtrees. We experimentally evaluate design tradeoffs, and demonstrate that UQDT can maximize throughput by preventing any overlay network node from becoming a bottleneck.