초록

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Abstract ▼ AI-Helper

Recently, privacy preserving issues have been actively studied on the time-series data that are widely used in a variety of application such as financial, medical, and weather analysis. In this paper, we survey and analyze the recent works of privacy-preserving data mining on time-series data. For t...

Recently, privacy preserving issues have been actively studied on the time-series data that are widely used in a variety of application such as financial, medical, and weather analysis. In this paper, we survey and analyze the recent works of privacy-preserving data mining on time-series data. For this. first we investigate what is the privacy in time-series data. We then survey various perturbation techniques on time-series data in the centralized computing environment. We next investigate SMC(secure multiparty computation) and encryption techniques in the distributed computing environment. Social network and cloud computing applications incur a large volume of sensitive data, and thus, privacy preserving techniques for exploiting these sensitive data have become much more substantial in many research areas. Our survey results can be used for developing efficient and robust time-series based PPDM techniques that can be applied in the new computing environment.

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