Resumen de Privacy homomorphisms for statistical confidentiality
When publishing contingency tables which contain official statistics, a need to preserve statistical confidentiality arises. Statistical disclosure of individual units must be prevented. There is a wide choice of techniques to achieve this anonymization: cell supression, cell perturbation, etc. In this paper, we tackle the problem of using anonymized data to compute exact statistics; our approach is based on privacy homomorphisms, which are encryption transformations such that the decryption of a function of cyphers is a (possibly different) function of the corresponding clear messages. A new privacy homomorphism is presented and combined with some anonymization techniques, in order for a classified level to retrieve exact statistics from statistics computed on disclosure-protected data at an unclassified level
