Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure

Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be eas...
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Autor*in:	Blanquer, Ignacio [verfasserIn] Brasileiro, Francisco [verfasserIn] Brito, Andrey [verfasserIn] Calatrava, Amanda [verfasserIn] Carvalho, André [verfasserIn] Fetzer, Christof [verfasserIn] Figueiredo, Flavio [verfasserIn] Guimarães, Ronny Petterson [verfasserIn] Marinho, Leandro [verfasserIn] Meira, Wagner [verfasserIn] Silva, Altigran [verfasserIn] Alberich-Bayarri, Ángel [verfasserIn] Camacho-Ramos, Eduardo [verfasserIn] Jimenez-Pastor, Ana [verfasserIn] Ribeiro, Antonio Luiz L. [verfasserIn] Nascimento, Bruno Ramos [verfasserIn] Silva, Fábio [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Trustworthy cloud services Federated clouds Medical imaging

Übergeordnetes Werk:	Enthalten in: Future generation computer systems - Amsterdam [u.a.] : Elsevier Science, 1984, 110, Seite 119-134
Übergeordnetes Werk:	volume:110 ; pages:119-134

DOI / URN:	10.1016/j.future.2020.04.012

Katalog-ID:	ELV00427895X

Internformat


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100	1		\|a Blanquer, Ignacio \|e verfasserin \|4 aut
245	1	0	\|a Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure
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520			\|a Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project.
650		4	\|a Trustworthy cloud services
650		4	\|a Federated clouds
650		4	\|a Medical imaging
700	1		\|a Brasileiro, Francisco \|e verfasserin \|4 aut
700	1		\|a Brito, Andrey \|e verfasserin \|4 aut
700	1		\|a Calatrava, Amanda \|e verfasserin \|4 aut
700	1		\|a Carvalho, André \|e verfasserin \|4 aut
700	1		\|a Fetzer, Christof \|e verfasserin \|4 aut
700	1		\|a Figueiredo, Flavio \|e verfasserin \|4 aut
700	1		\|a Guimarães, Ronny Petterson \|e verfasserin \|4 aut
700	1		\|a Marinho, Leandro \|e verfasserin \|4 aut
700	1		\|a Meira, Wagner \|e verfasserin \|4 aut
700	1		\|a Silva, Altigran \|e verfasserin \|4 aut
700	1		\|a Alberich-Bayarri, Ángel \|e verfasserin \|4 aut
700	1		\|a Camacho-Ramos, Eduardo \|e verfasserin \|4 aut
700	1		\|a Jimenez-Pastor, Ana \|e verfasserin \|4 aut
700	1		\|a Ribeiro, Antonio Luiz L. \|e verfasserin \|4 aut
700	1		\|a Nascimento, Bruno Ramos \|e verfasserin \|4 aut
700	1		\|a Silva, Fábio \|e verfasserin \|4 aut
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publishDate	2020
allfields	10.1016/j.future.2020.04.012 doi (DE-627)ELV00427895X (ELSEVIER)S0167-739X(19)31804-7 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Blanquer, Ignacio verfasserin aut Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project. Trustworthy cloud services Federated clouds Medical imaging Brasileiro, Francisco verfasserin aut Brito, Andrey verfasserin aut Calatrava, Amanda verfasserin aut Carvalho, André verfasserin aut Fetzer, Christof verfasserin aut Figueiredo, Flavio verfasserin aut Guimarães, Ronny Petterson verfasserin aut Marinho, Leandro verfasserin aut Meira, Wagner verfasserin aut Silva, Altigran verfasserin aut Alberich-Bayarri, Ángel verfasserin aut Camacho-Ramos, Eduardo verfasserin aut Jimenez-Pastor, Ana verfasserin aut Ribeiro, Antonio Luiz L. verfasserin aut Nascimento, Bruno Ramos verfasserin aut Silva, Fábio verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 110, Seite 119-134 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:110 pages:119-134 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 110 119-134
spelling	10.1016/j.future.2020.04.012 doi (DE-627)ELV00427895X (ELSEVIER)S0167-739X(19)31804-7 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Blanquer, Ignacio verfasserin aut Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project. Trustworthy cloud services Federated clouds Medical imaging Brasileiro, Francisco verfasserin aut Brito, Andrey verfasserin aut Calatrava, Amanda verfasserin aut Carvalho, André verfasserin aut Fetzer, Christof verfasserin aut Figueiredo, Flavio verfasserin aut Guimarães, Ronny Petterson verfasserin aut Marinho, Leandro verfasserin aut Meira, Wagner verfasserin aut Silva, Altigran verfasserin aut Alberich-Bayarri, Ángel verfasserin aut Camacho-Ramos, Eduardo verfasserin aut Jimenez-Pastor, Ana verfasserin aut Ribeiro, Antonio Luiz L. verfasserin aut Nascimento, Bruno Ramos verfasserin aut Silva, Fábio verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 110, Seite 119-134 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:110 pages:119-134 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 110 119-134
allfields_unstemmed	10.1016/j.future.2020.04.012 doi (DE-627)ELV00427895X (ELSEVIER)S0167-739X(19)31804-7 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Blanquer, Ignacio verfasserin aut Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project. Trustworthy cloud services Federated clouds Medical imaging Brasileiro, Francisco verfasserin aut Brito, Andrey verfasserin aut Calatrava, Amanda verfasserin aut Carvalho, André verfasserin aut Fetzer, Christof verfasserin aut Figueiredo, Flavio verfasserin aut Guimarães, Ronny Petterson verfasserin aut Marinho, Leandro verfasserin aut Meira, Wagner verfasserin aut Silva, Altigran verfasserin aut Alberich-Bayarri, Ángel verfasserin aut Camacho-Ramos, Eduardo verfasserin aut Jimenez-Pastor, Ana verfasserin aut Ribeiro, Antonio Luiz L. verfasserin aut Nascimento, Bruno Ramos verfasserin aut Silva, Fábio verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 110, Seite 119-134 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:110 pages:119-134 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 110 119-134
allfieldsGer	10.1016/j.future.2020.04.012 doi (DE-627)ELV00427895X (ELSEVIER)S0167-739X(19)31804-7 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Blanquer, Ignacio verfasserin aut Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project. Trustworthy cloud services Federated clouds Medical imaging Brasileiro, Francisco verfasserin aut Brito, Andrey verfasserin aut Calatrava, Amanda verfasserin aut Carvalho, André verfasserin aut Fetzer, Christof verfasserin aut Figueiredo, Flavio verfasserin aut Guimarães, Ronny Petterson verfasserin aut Marinho, Leandro verfasserin aut Meira, Wagner verfasserin aut Silva, Altigran verfasserin aut Alberich-Bayarri, Ángel verfasserin aut Camacho-Ramos, Eduardo verfasserin aut Jimenez-Pastor, Ana verfasserin aut Ribeiro, Antonio Luiz L. verfasserin aut Nascimento, Bruno Ramos verfasserin aut Silva, Fábio verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 110, Seite 119-134 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:110 pages:119-134 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 110 119-134
allfieldsSound	10.1016/j.future.2020.04.012 doi (DE-627)ELV00427895X (ELSEVIER)S0167-739X(19)31804-7 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Blanquer, Ignacio verfasserin aut Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project. Trustworthy cloud services Federated clouds Medical imaging Brasileiro, Francisco verfasserin aut Brito, Andrey verfasserin aut Calatrava, Amanda verfasserin aut Carvalho, André verfasserin aut Fetzer, Christof verfasserin aut Figueiredo, Flavio verfasserin aut Guimarães, Ronny Petterson verfasserin aut Marinho, Leandro verfasserin aut Meira, Wagner verfasserin aut Silva, Altigran verfasserin aut Alberich-Bayarri, Ángel verfasserin aut Camacho-Ramos, Eduardo verfasserin aut Jimenez-Pastor, Ana verfasserin aut Ribeiro, Antonio Luiz L. verfasserin aut Nascimento, Bruno Ramos verfasserin aut Silva, Fábio verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 110, Seite 119-134 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:110 pages:119-134 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 110 119-134
language	English
source	Enthalten in Future generation computer systems 110, Seite 119-134 volume:110 pages:119-134
sourceStr	Enthalten in Future generation computer systems 110, Seite 119-134 volume:110 pages:119-134
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Trustworthy cloud services Federated clouds Medical imaging
dewey-raw	004
isfreeaccess_bool	false
container_title	Future generation computer systems
authorswithroles_txt_mv	Blanquer, Ignacio @@aut@@ Brasileiro, Francisco @@aut@@ Brito, Andrey @@aut@@ Calatrava, Amanda @@aut@@ Carvalho, André @@aut@@ Fetzer, Christof @@aut@@ Figueiredo, Flavio @@aut@@ Guimarães, Ronny Petterson @@aut@@ Marinho, Leandro @@aut@@ Meira, Wagner @@aut@@ Silva, Altigran @@aut@@ Alberich-Bayarri, Ángel @@aut@@ Camacho-Ramos, Eduardo @@aut@@ Jimenez-Pastor, Ana @@aut@@ Ribeiro, Antonio Luiz L. @@aut@@ Nascimento, Bruno Ramos @@aut@@ Silva, Fábio @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	320604284
dewey-sort	14
id	ELV00427895X
language_de	englisch
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author	Blanquer, Ignacio
spellingShingle	Blanquer, Ignacio ddc 004 bkl 54.00 misc Trustworthy cloud services misc Federated clouds misc Medical imaging Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure
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topic_title	004 DE-600 54.00 bkl Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure Trustworthy cloud services Federated clouds Medical imaging
topic	ddc 004 bkl 54.00 misc Trustworthy cloud services misc Federated clouds misc Medical imaging
topic_unstemmed	ddc 004 bkl 54.00 misc Trustworthy cloud services misc Federated clouds misc Medical imaging
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title	Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure
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title_full	Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure
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author_browse	Blanquer, Ignacio Brasileiro, Francisco Brito, Andrey Calatrava, Amanda Carvalho, André Fetzer, Christof Figueiredo, Flavio Guimarães, Ronny Petterson Marinho, Leandro Meira, Wagner Silva, Altigran Alberich-Bayarri, Ángel Camacho-Ramos, Eduardo Jimenez-Pastor, Ana Ribeiro, Antonio Luiz L. Nascimento, Bruno Ramos Silva, Fábio
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title_sort	federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure
title_auth	Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure
abstract	Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project.
abstractGer	Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project.
abstract_unstemmed	Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project.
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title_short	Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure
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author2	Brasileiro, Francisco Brito, Andrey Calatrava, Amanda Carvalho, André Fetzer, Christof Figueiredo, Flavio Guimarães, Ronny Petterson Marinho, Leandro Meira, Wagner Silva, Altigran Alberich-Bayarri, Ángel Camacho-Ramos, Eduardo Jimenez-Pastor, Ana Ribeiro, Antonio Luiz L. Nascimento, Bruno Ramos Silva, Fábio
author2Str	Brasileiro, Francisco Brito, Andrey Calatrava, Amanda Carvalho, André Fetzer, Christof Figueiredo, Flavio Guimarães, Ronny Petterson Marinho, Leandro Meira, Wagner Silva, Altigran Alberich-Bayarri, Ángel Camacho-Ramos, Eduardo Jimenez-Pastor, Ana Ribeiro, Antonio Luiz L. Nascimento, Bruno Ramos Silva, Fábio
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up_date	2024-07-06T22:27:26.933Z
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Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. 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