Computation of privacy-preserving prices in smart grids
Fragkiskos Koufogiannis, Shuo Han, George Pappas

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.

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  • 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.
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