Probabilistic Metric Spaces for Privacy by Design Machine Learning Algorithms
Machine learning, data mining and statistics are used to analyze the data and to build models from them. Data privacy for big data needs to find a compromise between data analysis and disclosure risk. Privacy by design machine learning algorithms need to take into account the space of models and the...
| Authors: | , |
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| Format: | book part |
| Publication Date: | 2018 |
| Country: | España |
| Institution: | Universitat Autònoma de Barcelona |
| Repository: | Dipòsit Digital de Documents de la UAB |
| Language: | English |
| OAI Identifier: | oai:ddd.uab.cat:203044 |
| Online Access: | https://ddd.uab.cat/record/203044 https://dx.doi.org/urn:doi:10.1007/978-3-030-00305-0_30 |
| Access Level: | Open access |
| Keyword: | Data privacy Integral privacy Probabilistic metric spaces |
| Summary: | Machine learning, data mining and statistics are used to analyze the data and to build models from them. Data privacy for big data needs to find a compromise between data analysis and disclosure risk. Privacy by design machine learning algorithms need to take into account the space of models and the relationship between the data that generates the models and the models themselves. In this paper we propose the use of probabilistic metric spaces for comparing these models. |
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