A Pragmatic Approach for Privacy Preserving Healthcare Using Stretched Homomorphic Re-Encryption Decryption Algorithm

Abstract

   On the increasing Big Data accessibility of open uncovered records, to the security of user privacy have become an essential problem. In several Big data applications, the datasets often increase with time due to the addition of new data (i.e. incremental/scalable data). The current Big Data platforms has huge number of operational risks. While the most privacy preserving the existing models use to access the data in Big Data platforms cannot resist the attacks. Hackers  may gain access to the data, and identify the particular environment of a person’s information, which may lead to the disclosure of citizen’s private information. However to improve the security level of a scalable Big data platform privacy-preserving scheme is proposed. Here, the data security is optimized by adding Laplacian noise and encryption, decryption are done using homomorphic re-encryption and re-decryption method. The proposed technique is implemented using Python and the comparison is done between Augmented Homomorphic Re-Encryption Decryption (AHRED) and the proposed Stretched Homomorphic Re-Encryption Decryption (SHRED) algorithms. The SHRED algorithm provides an enhanced performance than the AHRED algorithm.