Towards a private vector space model for confidential documents

Authors:
Daniel Abril;Guillermo Navarro-Arribas;Vicenç Torra
Affiliations:
Institut d'Investigació en Intel ligència Artificial (IIIA), Investigaciones Científicas (CSIC);Universitat Autònoma de Barcelona (UAB);Institut d'Investigació en Intel ligència Artificial (IIIA), Investigaciones Científicas (CSIC)
Venue:
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Year:
2013

Citing 4
Cited 0

Protecting Respondents' Identities in Microdata Release

IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control

IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Introduction to Information Retrieval

Introduction to Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce in this paper a method to anonymize document vector spaces. These vector spaces can be used to analyze confidential documents without disclosing private information. The method is inspired in microaggregation, a popular technique used in statistical disclosure control.