DocumentCode :
244817
Title :
Secure and Efficient Data Integrity Based on Iris Features in Cloud Computing
Author :
Abbdal, Salah H. ; Hai Jin ; Deqing Zou ; Yassin, Ali A.
Author_Institution :
Services Comput. Technol. & Syst. Lab. Cluster & Grid Comput. Lab., Huazhong Univ. of Sci. & Technol., Wuhan, China
fYear :
2014
fDate :
20-23 Dec. 2014
Firstpage :
3
Lastpage :
6
Abstract :
Cloud computing aids users to outsource their data in the cloud remotely to prevent them from burdens of local storage and maintenance. Users no longer have possession and control of these data. This property brings many new security challenges like unauthorised entities and correctness of stored data. In this paper, we focus on the problem of ensuring the integrity of data stored in the cloud. We propose a method which combines biometric and cryptography techniques in a cost-effective manner for data owners to gain trust in the cloud. We present efficient and secure integrity based on the XOR operation and iris feature extraction as the strong factors. This work gives the cloud user more confidence in detecting any block that has been changed. Additionally, our proposed scheme employs user´s iris features to secure and integrate data in a manner difficult for any internal or external entity to take or compromise it. Extensive security and performance analysis show that our scheme is highly efficient and provably secure.
Keywords :
cloud computing; cryptography; data integrity; feature extraction; iris recognition; XOR operation; biometric technique; cloud computing; cryptography technique; data integrity security; data outsourcing; external entity; internal entity; iris feature extraction; iris features; performance analysis; Cloud computing; Feature extraction; Iris; Iris recognition; Security; Servers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Security Technology (SecTech), 2014 7th International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-1-4799-7775-8
Type :
conf
DOI :
10.1109/SecTech.2014.8
Filename :
7023272
Link To Document :
بازگشت