Secure and Privacy Preserving Mail Servers using Modified Homomorphic Encryption (MHE) Scheme

Abstract

Electronic mail (Email) or the paperless mail is becoming the most acceptable, faster and cheapest way of formal and informal information sharing between users. Around 500 billion mails are sent each day and the count is expected to be increasing. Today, even the sensitive and private information are shared through emails, thus making it the primary target for attackers and hackers. Also, the companies having their own mail server, relies on cloud system for storing the mails at a lower cost and maintenance. This affected the privacy of users as the searching pattern is visible to the cloud. To rectify this, we need to have a secure architecture for storing the emails and retrieve them according to the user queries. Data as well as the queries and computations to retrieve the relevant mails should be hidden from the third party. This article proposes a modified homomorphic encryption (MHE) technique to secure the mails. Homomorphic encryption is made practical using MHE and by incorporating Map Reduce parallel programming model, the execution time is exponentially reduced. Well known techniques in information retrieval, like Vector Space model and Term Frequency -Inverse Document Frequency (TF-IDF) concepts are utilized for finding relevant mails to the query. The analysis done on the dataset proves that our method is efficient in terms of execution time and in ensuring the security of the data and the privacy of the users.

Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics

“…The estimated workload Big Data rapidly enlarge as the amount of evaluation trading operations was combined with the number of variables active in the calculation [13], [14]. In addition, our tensor-supported FHE was separate from the lattice-based homomorphic approach [12]developed a tensor-based FHE that applied a data holder-centric mechanism and empower all data to be encrypted in the primary Big Data while supporting accurate manipulations.…”