### Resumé

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.

Originalsprog | Dansk |
---|---|

Titel | 2019 18th European Control Conference (ECC) |

Antal sider | 6 |

Forlag | IEEE |

Publikationsdato | 15 aug. 2019 |

Sider | 3490-3495 |

ISBN (Trykt) | 978-1-7281-1314-2 |

ISBN (Elektronisk) | 978-3-907144-00-8 |

DOI | |

Status | Udgivet - 15 aug. 2019 |

Begivenhed | 2019 18th European Control Conference (ECC) - Napoli, Italien Varighed: 25 jun. 2019 → 28 jun. 2019 |

### Konference

Konference | 2019 18th European Control Conference (ECC) |
---|---|

Land | Italien |

By | Napoli |

Periode | 25/06/2019 → 28/06/2019 |

### Emneord

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

### Citer dette

*2019 18th European Control Conference (ECC)*(s. 3490-3495). IEEE. https://doi.org/10.23919/ECC.2019.8796169

}

*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.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Privacy Preserving Recursive Least Squares Solutions

AU - Mølgaard, Katrine Tjell

AU - Cascudo, Ignacio

AU - Wisniewski, Rafal

PY - 2019/8/15

Y1 - 2019/8/15

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.

KW - privacy

KW - multiparty computation

KW - secret sharing

KW - Recursive Least Squares

U2 - 10.23919/ECC.2019.8796169

DO - 10.23919/ECC.2019.8796169

M3 - Konferenceartikel i proceeding

SN - 978-1-7281-1314-2

SP - 3490

EP - 3495

BT - 2019 18th European Control Conference (ECC)

PB - IEEE

ER -