The paper's aim is to present a novel anonymity quantification method, LooM (loosely managed privacy protection method) for achieving privacy protection in pervasive computing environments.
The main feature is that the method quantitatively controls anonymity by a single value (disclosure threshold value) using a classification algorithm of the decision tree. The value is not affected by the set size of users or the distribution of users' private information. The effectiveness of this method is confirmed by simulation using sample databases of attribute‐value pairs. Proposes a model of privacy information disclosure that achieves a balance between users' privacy protection requirements and service providers' disclosure requirements and applies web questionnaire survey data to this model.
LooM can be applicable to a variety of pervasive computing and communication services handling a huge amount of data containing privacy information.
The paper proposes a model of privacy information disclosure that achieves a balance between users' privacy protection requirements and service providers' disclosure requirements.
Imada, M., Ohta, M., Yamaguchi, M. and Yong Kim, S. (2008), "LooM: An anonymity quantification method in pervasive computing environments", International Journal of Pervasive Computing and Communications, Vol. 4 No. 1, pp. 110-123. https://doi.org/10.1108/17427370810873147Download as .RIS
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