Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymity
The focus of data anonymity research by computer scientists is almost completely on methods with formal guarantees of anonymity, especially differential privacy. The usefulness of mechanisms with formal guarantees, however, has so far been disappointing. This article argues that computer scientists...
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | IE |
| Repositorio: | Repositorio IE |
| OAI Identifier: | oai:repositorio.ie.edu:20.500.14417/2767 |
| Acceso en línea: | https://doi.org/10.5281/zenodo.3731250 https://hdl.handle.net/20.500.14417/2767 |
| Access Level: | acceso abierto |
| Palabra clave: | Data set Conjuntos de datos Analysis Personal Data Datos personales Privacy Privacidad Marketing Technology Tecnología General Data Protection Regulation GDPR Regulación General de Protección de Datos RGPD |
| Sumario: | The focus of data anonymity research by computer scientists is almost completely on methods with formal guarantees of anonymity, especially differential privacy. The usefulness of mechanisms with formal guarantees, however, has so far been disappointing. This article argues that computer scientists should be open to and encouraged to work on empirical data anonymization mechanisms as well—in much the same way that researchers work on both formal and empirical approaches to crypto. This article describes differential privacy and explains its benefits and shortcomings. It also describes a recently developed empirical data anonymization mechanism called Diffix, and describes how transparency and programs that incentivize white-hat attacks, such as bounty programs, can build understanding and confidence in empirical approaches. The article concludes that there is a need for both formal and empirical research on data anonymity. |
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