Abstract
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.
X.-Y. Li—The research is partially supported by NSF ECCS-1247944 and NSF CMMI 1436786.
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Jung, T., Li, XY., Han, J. (2015). A Framework for Optimization in Big Data: Privacy-Preserving Multi-agent Greedy Algorithm. In: Wang, Y., Xiong, H., Argamon, S., Li, X., Li, J. (eds) Big Data Computing and Communications. BigCom 2015. Lecture Notes in Computer Science(), vol 9196. Springer, Cham. https://doi.org/10.1007/978-3-319-22047-5_8
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DOI: https://doi.org/10.1007/978-3-319-22047-5_8
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