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This project aims at providing a comprehensive, open source anonymization framework for sensitive personal data. It is able to alter the data in a way that guarantees minimal information loss while making sure that the transformed data adheres to well-known privacy criteria, such as k-anonymity, l-diversity or t-closeness.
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saetre/arx
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INTRODUCTION ************ This project aims at providing a comprehensive, open source anonymization framework for sensitive personal data. It is able to alter the data in a way that guarantees minimal information loss while making sure that the transformed data adheres to well-known privacy criteria, such as k-anonymity, l-diversity or t-closeness. It implements a variety of globally optimal full-domain anonymity algorithms and implements several optimizations which result in a highly efficient anonymization process. This includes an implementation of the Flash algorithm which uses a novel search strategy and fully exploits the implementation framework while offering stable execution times. More details can be found at: http://arx.deidentifier.org/ LICENSE ******* The ARX framework is copyright (C) 2012 Florian Kohlmayer and Fabian Prasser. It is licensed under the GNU GPL3: This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/. EXTERNAL LIBRARIES ****************** The framework uses external libraries. The according licenses are listed in the respective lib folders.
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This project aims at providing a comprehensive, open source anonymization framework for sensitive personal data. It is able to alter the data in a way that guarantees minimal information loss while making sure that the transformed data adheres to well-known privacy criteria, such as k-anonymity, l-diversity or t-closeness.
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