lomas-server-0.0.29

Lomas is a remote access platform developed by the Swiss Federal Statistical Office allowing National Statistical Offices to offer eyes-off data science on private datasets while controlling disclosure risk. The platform relies on a service that is meant to be deployed on-premises and allows accredited users to apply differentially private algorithms on private datasets using a dedicated client Python library.

lomas-server-0.0.27

Lomas is a remote access platform developed by the Swiss Federal Statistical Office allowing National Statistical Offices to offer eyes-off data science on private datasets while controlling disclosure risk. The platform relies on a service that is meant to be deployed on-premises and allows accredited users to apply differentially private algorithms on private datasets using a dedicated client Python library.

lomas-server-0.0.26

Lomas is a remote access platform developed by the Swiss Federal Statistical Office allowing National Statistical Offices to offer eyes-off data science on private datasets while controlling disclosure risk. The platform relies on a service that is meant to be deployed on-premises and allows accredited users to apply differentially private algorithms on private datasets using a dedicated client Python library.

lomas-server-0.0.25

Lomas is a remote access platform developed by the Swiss Federal Statistical Office allowing National Statistical Offices to offer eyes-off data science on private datasets while controlling disclosure risk. The platform relies on a service that is meant to be deployed on-premises and allows accredited users to apply differentially private algorithms on private datasets using a dedicated client Python library.

lomas-server-0.0.24

Lomas is a remote access platform developed by the Swiss Federal Statistical Office allowing National Statistical Offices to offer eyes-off data science on private datasets while controlling disclosure risk. The platform relies on a service that is meant to be deployed on-premises and allows accredited users to apply differentially private algorithms on private datasets using a dedicated client Python library.

lomas-server-0.0.23

Lomas is a remote access platform developed by the Swiss Federal Statistical Office allowing National Statistical Offices to offer eyes-off data science on private datasets while controlling disclosure risk. The platform relies on a service that is meant to be deployed on-premises and allows accredited users to apply differentially private algorithms on private datasets using a dedicated client Python library.

lomas-server-0.0.22

Lomas is a remote access platform developed by the Swiss Federal Statistical Office allowing National Statistical Offices to offer eyes-off data science on private datasets while controlling disclosure risk. The platform relies on a service that is meant to be deployed on-premises and allows accredited users to apply differentially private algorithms on private datasets using a dedicated client Python library.

lomas-server-0.0.21

Lomas is a remote access platform developed by the Swiss Federal Statistical Office allowing National Statistical Offices to offer eyes-off data science on private datasets while controlling disclosure risk. The platform relies on a service that is meant to be deployed on-premises and allows accredited users to apply differentially private algorithms on private datasets using a dedicated client Python library.

lomas-server-0.0.20

Lomas is a remote access platform developed by the Swiss Federal Statistical Office allowing National Statistical Offices to offer eyes-off data science on private datasets while controlling disclosure risk. The platform relies on a service that is meant to be deployed on-premises and allows accredited users to apply differentially private algorithms on private datasets using a dedicated client Python library.

lomas-server-0.0.19

Lomas is a remote access platform developed by the Swiss Federal Statistical Office allowing National Statistical Offices to offer eyes-off data science on private datasets while controlling disclosure risk. The platform relies on a service that is meant to be deployed on-premises and allows accredited users to apply differentially private algorithms on private datasets using a dedicated client Python library.