Publishing Set Valued Data via m-Privacy

It is very important to achieve security of data in distributed databases. With increasing in the usability of distributed database security issues regarding it are also going to be more complex. M-privacy is a very effective technique which may be used to achieve security of distributed databases. Set-valued data provides huge opportunities for a variety of data mining tasks. Most of the present data publishing techniques for set-valued data are refers to horizontal division based privacy models. Differential privacy method is totally opposite to horizontal based privacy method; it provides higher privacy guarantee and it is also so vereign of an adversary's environment information and computational capability. Set-valued data have high dimensionality so not any single existing data publishing approach for differential privacy can be applied for both utility and scalability. This work provided detailed information about this new threat, and gave some assistance to resolve it. At the start we introduced the concept of m-privacy. This concept guarantees that the anonymous data will satisfies a given privacy check next to any group of up to m colluding data providers. After it we presented heuristic approach for exploiting the monotonicity of confidentiality constraints for proficiently inspecting m-privacy given a cluster of records. Next, we have presented a data provider-aware anonymization approach with adaptive m-privacy inspection strategies to guarantee high usefulness and m-privacy of anonymized data with effectiveness. Finally, we proposed secured multi-party calculation protocols for set valued data publishing with m-privacy.