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Conference Papers Year : 2012

Towards a Framework to Achieve Privacy Preserving Data Mining Using Medical Classifications-Driven Data Generalisation

Abstract

Privacy-preserving data mining often focuses on data alteration but may bias data patterns interpretation and does not offer different levels of access to patterns according to their use. This paper addresses data mining as a prediction tool and proposes to offer several levels of access to data patterns according to users' trustworthiness. The grounding intuition is that patterns' predictive value depends on their precision that should thus vary according to their use. The following problem is considered: a medical data holder wants to disclose data or data patterns and still control the meaning of the disclosed patterns or of the patterns that may be mined out of the released dataset. To tackle this issue, we propose a framework compliant with existing data mining techniques by modeling trust in terms of data precision and generalising data according to standard medical classifications.
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Dates and versions

hal-00953417 , version 1 (28-02-2014)

Identifiers

  • HAL Id : hal-00953417 , version 1

Cite

Aurélien Faravelon, Christine Verdier. Towards a Framework to Achieve Privacy Preserving Data Mining Using Medical Classifications-Driven Data Generalisation. 3rd International ICST Conference on Electronic Healthcare for the 21st Century, 2010, Casablanca, Morocco. pp.204-211. ⟨hal-00953417⟩
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