Abstract Security of data stored in the cloud databases is a challenging and complex issue to be addressed due to the presence of malicious attacks, data breaches and unsecured access points. In the past, many researchers proposed security mechanisms including access control, intrusion detection and prevention models, Encryption based storage methods and key management schemes. However, the role based access control policies that were developed to provide security for the data stored in cloud databases based on the sensitivity of the information are compromised by the attackers through the misuse of privileges gained by them from multiple roles. Therefore, it is necessary to propose more efficient mechanisms for securing the sensitive information through attribute based encryption by analyzing the association between the various attributes. For handling the security issue related to the large volume of cloud data effectively, the association rule mining algorithm has been extended with temporal constraints in this work in order to find the association among the attributes so that it is possible to form groups among the attributes as public attributes with insensitive data, group attributes with medium sensitive data and owner with highly sensitive attributes and data for enhancing the strength of attribute based encryption scheme. Based on the associations among the attributes and temporal constraints, it is possible to encrypt the sensitive data with stronger keys and algorithms. Hence, a new key generation and encryption algorithm is proposed in this paper by combining the Greatest common divisor and the Least common multiple between the primary key value and the first numeric non key attribute that is medium sensitive attributes and data present in the cloud database for providing secured storage through effective attribute based encryption. Moreover, a new intelligent algorithm called Elliptic Curve Cryptography with Base100 Table algorithm is also proposed in this paper for performing encryption and decryption operations over the most sensitive data for the data owners. From the experiments conducted in this work, it is observed that the proposed model enhances the data security by more than 5% when it is compared with other existing secured storage models available for cloud.

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[32] Zhong S., “Privacy-Preserving Algorithms for Distributed Mining of Frequent Item Sets,” Information Sciences, vol. 177, no. 2, pp. 490- 503, 2007. Pradeep Suthanthiramani is pursuing Ph.D in DIST, CEG Campus, Anna University, Chennai, India. He has completed his M.Sc in Madurai Kamaraj University, Madurai, India. His areas of interests are cryptography and network security. Muthurajkumar Sannasy is working as an Assistant Professor in DCT at MIT Campus, Anna University, Chennai, India. He has completed his M.E and Ph.D degrees in Anna University, Chennai. His areas of interests are including cloud security. Ganapathy Sannasi is working as Senior Assistant Professor (Grade- II) in SCOPE and Research Center for Cyber Physical Systems at VIT- Chennai, Chennai, India. He has completed his M.E and Ph.D degrees in Anna University, Chennai. His areas of interests are including network security. Kannan Arputharaj is working as a Senior Professor in SCOPE at VIT- Vellore, Vellore, India. He has completed his M.E and Ph.D degrees in Anna University, Chennai. He is a Retired Professor of Anna University, Chennai. His areas of interests are including data mining, cryptography and network security.