Privacy Preserving Recursive Least Squares Solutions

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Resumé

Individual privacy is becoming a more prioritized issue in the modern world, because the world is becoming increasingly more digitized and citizens are
starting to feel monitored. Private information could furthermore be misused in the wrong hands. Many control systems rely on data that often contain privacy sensitive information. These are systems such as the power grid, water network, and smart house where data contain individual consumption profiles and daily schedules. The systems use the data to compute optimized solutions; hence, the data is valuable but it contains private information. To this end, it is desirable to achieve algorithms able to calculate optimized solutions while keeping the data secret. As a step towards this goal, we propose a privacy preserving recursive least squares protocol that computes a least squares estimate of the parameters of a linear system based on observations of input and outputs. This estimate is calculated while ensuring no leakage of information about observations.
OriginalsprogDansk
Titel2019 18th European Control Conference (ECC)
Antal sider6
ForlagIEEE
Publikationsdato15 aug. 2019
Sider3490-3495
ISBN (Trykt)978-1-7281-1314-2
ISBN (Elektronisk)978-3-907144-00-8
DOI
StatusUdgivet - 15 aug. 2019
Begivenhed 2019 18th European Control Conference (ECC) - Napoli, Italien
Varighed: 25 jun. 201928 jun. 2019

Konference

Konference 2019 18th European Control Conference (ECC)
LandItalien
ByNapoli
Periode25/06/201928/06/2019

Emneord

  • privacy
  • multiparty computation
  • secret sharing
  • Recursive Least Squares

Citer dette

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title = "Privacy Preserving Recursive Least Squares Solutions",
abstract = "Individual privacy is becoming a more prioritized issue in the modern world, because the world is becoming increasingly more digitized and citizens are starting to feel monitored. Private information could furthermore be misused in the wrong hands. Many control systems rely on data that often contain privacy sensitive information. These are systems such as the power grid, water network, and smart house where data contain individual consumption profiles and daily schedules. The systems use the data to compute optimized solutions; hence, the data is valuable but it contains private information. To this end, it is desirable to achieve algorithms able to calculate optimized solutions while keeping the data secret. As a step towards this goal, we propose a privacy preserving recursive least squares protocol that computes a least squares estimate of the parameters of a linear system based on observations of input and outputs. This estimate is calculated while ensuring no leakage of information about observations.",
keywords = "privacy, multiparty computation, secret sharing, Recursive Least Squares",
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Mølgaard, KT, Cascudo, I & Wisniewski, R 2019, Privacy Preserving Recursive Least Squares Solutions. i 2019 18th European Control Conference (ECC). IEEE, s. 3490-3495, 2019 18th European Control Conference (ECC), Napoli, Italien, 25/06/2019. https://doi.org/10.23919/ECC.2019.8796169

Privacy Preserving Recursive Least Squares Solutions. / Mølgaard, Katrine Tjell; Cascudo, Ignacio; Wisniewski, Rafal.

2019 18th European Control Conference (ECC). IEEE, 2019. s. 3490-3495.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

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N2 - Individual privacy is becoming a more prioritized issue in the modern world, because the world is becoming increasingly more digitized and citizens are starting to feel monitored. Private information could furthermore be misused in the wrong hands. Many control systems rely on data that often contain privacy sensitive information. These are systems such as the power grid, water network, and smart house where data contain individual consumption profiles and daily schedules. The systems use the data to compute optimized solutions; hence, the data is valuable but it contains private information. To this end, it is desirable to achieve algorithms able to calculate optimized solutions while keeping the data secret. As a step towards this goal, we propose a privacy preserving recursive least squares protocol that computes a least squares estimate of the parameters of a linear system based on observations of input and outputs. This estimate is calculated while ensuring no leakage of information about observations.

AB - Individual privacy is becoming a more prioritized issue in the modern world, because the world is becoming increasingly more digitized and citizens are starting to feel monitored. Private information could furthermore be misused in the wrong hands. Many control systems rely on data that often contain privacy sensitive information. These are systems such as the power grid, water network, and smart house where data contain individual consumption profiles and daily schedules. The systems use the data to compute optimized solutions; hence, the data is valuable but it contains private information. To this end, it is desirable to achieve algorithms able to calculate optimized solutions while keeping the data secret. As a step towards this goal, we propose a privacy preserving recursive least squares protocol that computes a least squares estimate of the parameters of a linear system based on observations of input and outputs. This estimate is calculated while ensuring no leakage of information about observations.

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