A Proposal of Privacy-Preserving Data Aggregation on the Cloud Computing

Autor: Kouichi Itoh, Hiroshi Tsuda, Yoshinori Katayama, Mebae Ushdia, Fumihiko Kozakura
Rok vydání: 2013
Předmět:
Zdroj: NBiS
DOI: 10.1109/nbis.2013.24
Popis: In recent years, the evolution of the cloud computing increases the demand of the services which provides profitable information analyzed from huge data from heterogeneous information sources. In order to reduce the user's anxiety to store the private and confidential information in the cloud, various Privacy Preserving Data Mining (PPDM) techniques are developed, which enable the cloud to perform data mining from secured data. In this paper, we propose a PPDM technique specialized in data aggregation that satisfies following two requirements: (1)The information stored in the cloud is guaranteed against information leakage, because no profitable information is obtained from the secured data without the users' secret information, which is not stored in the cloud. (2)The cloud provides multi-level granularity of the aggregation results according to the user's authority.
Databáze: OpenAIRE