Camouflage Learning

Stephan Sigg; Le Ngu Nguyen; Jing Ma

doi:10.1109/PerComWorkshops51409.2021.9431111

Camouflage Learning

Stephan Sigg, Le Ngu Nguyen, Jing Ma

Ambient Intelligence

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

72 Lataukset (Pure)

Abstrakti

Federated learning has been proposed as a concept for distributed machine learning which enforces privacy by avoiding sharing private data with a coordinator or distributed nodes. However, information on local data might be leaked through the model updates. We propose Camouflage learning, a machine learning scheme that distributes both the data and the model. Neither the distributed devices nor the coordinator is at any point in time in possession of the complete model. Furthermore, data and model are obfuscated during distributed model inference and distributed model training. Camouflage learning can be implemented with various Machine learning schemes.

Alkuperäiskieli	Englanti
Otsikko	2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021
Kustantaja	IEEE
Sivut	724-729
Sivumäärä	6
ISBN (elektroninen)	978-1-6654-0424-2
DOI - pysyväislinkit	https://doi.org/10.1109/PerComWorkshops51409.2021.9431111
Tila	Julkaistu - 25 toukok. 2021
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Conference on Pervasive Computing and Communications Workshops - Kassel, Saksa Kesto: 22 maalisk. 2021 → 26 maalisk. 2021 https://www.percom.org/

Julkaisusarja

Nimi	IEEE international conference on pervasive computing and communications workshops
Kustantaja	IEEE

Conference

Conference	IEEE International Conference on Pervasive Computing and Communications Workshops
Lyhennettä	PerCom Workshops
Maa/Alue	Saksa
Kaupunki	Kassel
Ajanjakso	22/03/2021 → 26/03/2021
www-osoite	https://www.percom.org/

Pääsy asiakirjaan

10.1109/PerComWorkshops51409.2021.9431111

ELEC_Sigg_etal_Camouflage_Learning_IEEE_PerCom_2021Accepted author manuscript, 2,21 MB

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

Adaptiivinen ambientin signaalin takaisinsirontaan perustuva tietoliikenne erittäin matalatehoisille järjestelmille
Sigg, S. (Vastuullinen tutkija)
01/09/2018 → 31/08/2021
Projekti: Academy of Finland: Other research funding

Siteeraa tätä

Sigg, S., Nguyen, L. N., & Ma, J. (2021). Camouflage Learning. teoksessa 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021 (Sivut 724-729). Artikkeli 9431111 (IEEE international conference on pervasive computing and communications workshops). IEEE. https://doi.org/10.1109/PerComWorkshops51409.2021.9431111

@inproceedings{6e4b3f904c394176a2857e7fb57b8dbd,

title = "Camouflage Learning",

abstract = "Federated learning has been proposed as a concept for distributed machine learning which enforces privacy by avoiding sharing private data with a coordinator or distributed nodes. However, information on local data might be leaked through the model updates. We propose Camouflage learning, a machine learning scheme that distributes both the data and the model. Neither the distributed devices nor the coordinator is at any point in time in possession of the complete model. Furthermore, data and model are obfuscated during distributed model inference and distributed model training. Camouflage learning can be implemented with various Machine learning schemes. ",

keywords = "Distributed machine learning, Internet of Things, Multi-key homomorphic encryption, Privacy",

author = "Stephan Sigg and Nguyen, {Le Ngu} and Jing Ma",

note = "Funding Information: ACKNOWLEDGMENT We would like to acknowledge partial funding by the Academy of Finland Project ABACUS (ICT 2023). Publisher Copyright: {\textcopyright} 2021 IEEE.; IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops ; Conference date: 22-03-2021 Through 26-03-2021",

year = "2021",

month = may,

day = "25",

doi = "10.1109/PerComWorkshops51409.2021.9431111",

language = "English",

series = "IEEE international conference on pervasive computing and communications workshops",

publisher = "IEEE",

pages = "724--729",

booktitle = "2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021",

address = "United States",

url = "https://www.percom.org/",

}

Sigg, S, Nguyen, LN & Ma, J 2021, Camouflage Learning. julkaisussa 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021., 9431111, IEEE international conference on pervasive computing and communications workshops, IEEE, Sivut 724-729, IEEE International Conference on Pervasive Computing and Communications Workshops, Kassel, Hesse, Saksa, 22/03/2021. https://doi.org/10.1109/PerComWorkshops51409.2021.9431111

Camouflage Learning. / Sigg, Stephan; Nguyen, Le Ngu; Ma, Jing.
2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021. IEEE, 2021. s. 724-729 9431111 (IEEE international conference on pervasive computing and communications workshops).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Camouflage Learning

AU - Sigg, Stephan

AU - Nguyen, Le Ngu

AU - Ma, Jing

PY - 2021/5/25

Y1 - 2021/5/25

N2 - Federated learning has been proposed as a concept for distributed machine learning which enforces privacy by avoiding sharing private data with a coordinator or distributed nodes. However, information on local data might be leaked through the model updates. We propose Camouflage learning, a machine learning scheme that distributes both the data and the model. Neither the distributed devices nor the coordinator is at any point in time in possession of the complete model. Furthermore, data and model are obfuscated during distributed model inference and distributed model training. Camouflage learning can be implemented with various Machine learning schemes.

AB - Federated learning has been proposed as a concept for distributed machine learning which enforces privacy by avoiding sharing private data with a coordinator or distributed nodes. However, information on local data might be leaked through the model updates. We propose Camouflage learning, a machine learning scheme that distributes both the data and the model. Neither the distributed devices nor the coordinator is at any point in time in possession of the complete model. Furthermore, data and model are obfuscated during distributed model inference and distributed model training. Camouflage learning can be implemented with various Machine learning schemes.

KW - Distributed machine learning

KW - Internet of Things

KW - Multi-key homomorphic encryption

KW - Privacy

UR - http://www.scopus.com/inward/record.url?scp=85107590123&partnerID=8YFLogxK

U2 - 10.1109/PerComWorkshops51409.2021.9431111

DO - 10.1109/PerComWorkshops51409.2021.9431111

M3 - Conference article in proceedings

AN - SCOPUS:85107590123

T3 - IEEE international conference on pervasive computing and communications workshops

SP - 724

EP - 729

BT - 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021

PB - IEEE

T2 - IEEE International Conference on Pervasive Computing and Communications Workshops

Y2 - 22 March 2021 through 26 March 2021

ER -

Camouflage Learning

Abstrakti

Julkaisusarja

Conference

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

Adaptiivinen ambientin signaalin takaisinsirontaan perustuva tietoliikenne erittäin matalatehoisille järjestelmille

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