Team for Research in
Ubiquitous Secure Technology

Citation
Mikhail Lisovich, Deirdre Mulligan, Stephen Wicker. "Inferring Personal Information from Demand-Response Systems". IEEE Security & Privacy, 8(1):11-20, January 2010.

Abstract
Current and upcoming demand-response systems are providing increasingly detailed power consumption data to utilities and a growing array of players angling to assist consumers in understanding and managing their energy use. The granularity of this data, as well as the entry of new players into the energy market creates new privacy concerns. The detailed per-household consumption data generated by advanced metering systems reveals information about in-home activities which can be mined and combined with other readily available information to discover more about occupant's activities. We explore the technological aspects of this claim, focusing on the ways in which personally identifying information can be collected and repurposed. We begin with overviews of the energy-data-privacy landscape and a brief introduction to demand-response technologies. We proceed to discuss the motivations parties may have for repurposing collected data. We then prove that repurposing is feasible from a technical standpoint by conducting a small-scale experiment on a private residence. Our results show that, even with relatively unsophisticated hardware and data extraction algorithms, some information about occupant behavior can be estimated with a high degree of accuracy. We propose a disclosure metric to aid in quantifying the impact of data collection on in-home privacy, and construct an example metric for our experiment. Our paper concludes with a discussion of larger scale systems and guidelines for data-handling policies that advance the protection of privacy.

Electronic downloads

• HTML

  Mikhail Lisovich, Deirdre Mulligan, Stephen Wicker. <a
  href="http://www.truststc.org/pubs/662.html"
  >Inferring Personal Information from Demand-Response
  Systems</a>, <i>IEEE Security &
  Privacy</i>, 8(1):11-20, January 2010.

• Plain text

  Mikhail Lisovich, Deirdre Mulligan, Stephen Wicker.
  "Inferring Personal Information from Demand-Response
  Systems". <i>IEEE Security &
  Privacy</i>, 8(1):11-20, January 2010.

• BibTeX

  @article{LisovichMulliganWicker10_InferringPersonalInformationFromDemandResponseSystems,
      author = {Mikhail Lisovich and Deirdre Mulligan and Stephen
                Wicker},
      title = {Inferring Personal Information from
                Demand-Response Systems},
      journal = {IEEE Security \& Privacy},
      volume = {8},
      number = {1},
      pages = {11-20},
      month = {January},
      year = {2010},
      abstract = {Current and upcoming demand-response systems are
                providing increasingly detailed power consumption
                data to utilities and a growing array of players
                angling to assist consumers in understanding and
                managing their energy use. The granularity of this
                data, as well as the entry of new players into the
                energy market creates new privacy concerns. The
                detailed per-household consumption data generated
                by advanced metering systems reveals information
                about in-home activities which can be mined and
                combined with other readily available information
                to discover more about occupant's activities. We
                explore the technological aspects of this claim,
                focusing on the ways in which personally
                identifying information can be collected and
                repurposed. We begin with overviews of the
                energy-data-privacy landscape and a brief
                introduction to demand-response technologies. We
                proceed to discuss the motivations parties may
                have for repurposing collected data. We then prove
                that repurposing is feasible from a technical
                standpoint by conducting a small-scale experiment
                on a private residence. Our results show that,
                even with relatively unsophisticated hardware and
                data extraction algorithms, some information about
                occupant behavior can be estimated with a high
                degree of accuracy. We propose a disclosure metric
                to aid in quantifying the impact of data
                collection on in-home privacy, and construct an
                example metric for our experiment. Our paper
                concludes with a discussion of larger scale
                systems and guidelines for data-handling policies
                that advance the protection of privacy.},
      URL = {http://www.truststc.org/pubs/662.html}
  }
  

Posted by Mikhail Lisovich on 28 Mar 2010.
Groups: trust
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