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dc.contributor.authorShanu, P.K.
dc.contributor.authorChandrasekaran, K.
dc.identifier.citationProceedings - 2016 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2016, 2017, Vol., , pp.46-53en_US
dc.description.abstractOnline service providers started to providepersonalized recommendation to the users by collecting userprivate sensitive data. Traditionally the user private data isencrypted using a symmetric encryption algorithm beforestoring it in the cloud to provide another layer of security fordata at rest. It makes users' data secure from third parties, butnot the service provider. We propose a method that generatesrecommendations using homomorphically encrypted data in aprivacy preserved manner to provide protection against serviceprovider. We also verify the correctness of computations doneby a third parties and the service provider over encrypteddata using homomorphic authenticators and some securecryptographic protocols. � 2016 IEEE.en_US
dc.titleGenerating Privacy-Preserved Recommendation Using Homomorphic Authenticated Encryptionen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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