Dynamic Anonymous Index for Confidential Data

In this paper we introduce a $$k$$ k -anonymous vector space model, which can be used as an index of a set of confidential documents. This model allows to index, for example, encrypted data. New documents can be added or removed while maintaining the k-anonymity property of the vector space.

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