A Framework for Optimization in Big Data: Privacy-Preserving Multi-agent Greedy Algorithm

Due to the variety of the data source and the veracity of their trustworthiness, it is challenging to solve the distributed optimization problems in the big data applications owing to the privacy concerns. We propose a framework for distributed multi-agent greedy algorithms whereby any greedy algorithm that fits our requirement can be converted to a privacy-preserving one. After the conversion, the private information associated with each agent will not be disclosed to anyone else but the owner, and the same output as the plain greedy algorithm is computed by the converted one. Our theoretic analysis shows the security of the framework, and the implementation also shows good performance.

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