Citation
Fragkiskos Koufogiannis, Shuo Han, George Pappas. "Computation of privacy-preserving prices in smart grids". Conference on Decision and Control (CDC), 6, 20, March, 2014.

Abstract
Demand management through pricing is a modern approach that can improve the efficiency of modern power networks. However, computing optimal prices requires access to data that individuals consider private. We present a novel approach for computing prices while providing privacy guarantees under the differential privacy framework. Differentially private prices are computed through a distributed utility maximization problem with each individual perturbing her own utility function. Privacy concerning temporal localization and monitoring of individual's activity is enforced in the process. The proposed scheme provides formal privacy guarantees and its performance-privacy trade-off is evaluated numerically.

Electronic downloads

• https://www.src.org/library/publication/p069691/. (Link to paper on SRC website.)
• http://dx.doi.org/10.1109/CDC.2014.7039715

• HTML

  Fragkiskos Koufogiannis, Shuo Han, George Pappas. <a
  href="http://www.terraswarm.org/pubs/284.html"
  >Computation of privacy-preserving prices in smart
  grids</a>, Conference on Decision and Control (CDC),
  6, 20, March, 2014.

• Plain text

  Fragkiskos Koufogiannis, Shuo Han, George Pappas.
  "Computation of privacy-preserving prices in smart
  grids". Conference on Decision and Control (CDC), 6,
  20, March, 2014.

• BibTeX

  @inproceedings{KoufogiannisHanPappas14_ComputationOfPrivacypreservingPricesInSmartGrids,
      author = {Fragkiskos Koufogiannis and Shuo Han and George
                Pappas},
      title = {Computation of privacy-preserving prices in smart
                grids},
      booktitle = {Conference on Decision and Control (CDC)},
      pages = {6},
      day = {20},
      month = {March},
      year = {2014},
      abstract = {Demand management through pricing is a modern
                approach that can improve the efficiency of modern
                power networks. However, computing optimal prices
                requires access to data that individuals consider
                private. We present a novel approach for computing
                prices while providing privacy guarantees under
                the differential privacy framework. Differentially
                private prices are computed through a distributed
                utility maximization problem with each individual
                perturbing her own utility function. Privacy
                concerning temporal localization and monitoring of
                individual's activity is enforced in the process.
                The proposed scheme provides formal privacy
                guarantees and its performance-privacy trade-off
                is evaluated numerically.},
      URL = {http://terraswarm.org/pubs/284.html}
  }
  

Posted by Fragkiskos Koufogiannis on 21 Mar 2014.
Groups: services