Abstract

 The collaborative data publishing issue for anonymizing horizontally partitioned data at various data providers is considered. Another kind of “insider attack” by colluding data providers who may utilize their own particular data records (a subset of the general data) notwithstanding the external background knowledge to construe the data records helped by other data providers. This new risk makes a few commitments. The idea of m-privacy, which ensure that the anonymized data fulfils a given protection requrements against any group of up to m colluding data providers. Two algorithms for collaborative data are: one is a heuristic algorithm which is checking m-privacy anonymized data from the provider and second algorithm is provider aware anonymization which ensures m-privacy methodology and it is highest rated anonymized data providing efficiency. Secure multi-party computation (SMC) protocol and trusted third party (TTP) protocol can be used to guarantee that there is no disclosure of intermediate information during the anonymization. This protocol is used in the system at server side for keeping the data secure. Experiments on real-life datasets recommend that this methodology accomplishes better or similar utility and efficiency than existing and baseline algorithms while giving m-privacy ensure.

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