Accelerating Digital Forensics through Parallel Computing
Ghada Elkabbany, Mohamed Rasslan and Heba Aslan
DOI : 10.3844/jcssp.2018.238.252
Journal of Computer Science
Volume 14, Issue 2
Digital crimes in the era of big data and cloud computing imposes significant challenges in digital forensics. Cloud environment provides low cost, easy management and reasonable solutions. Moreover, it supports big data structures and solutions (i.e., security, privacy and digital forensics). In order to achieve a secure digital forensics analysis in cloud environment, researchers have proposed solutions with expensive communication cost and computation overheads. Among these solutions Nasereldin et al. proposed a protocol which solves the problem of authenticity and integrity of evidence using signcryption technique. This leads to low communication and implementation overheads. Furthermore, identity-based cryptography is used to solve Public Key Infrastructure (PKI) problems. In addition, it is characterized by the ability to divide the message into small messages which is suitable for pipelining techniques. Nasreldin et al.'s signcryption protocol is based on Elliptic Curve Cryptography (ECC) which is implemented by using different mathematical operations. In this protocol, ECC mathematical operations take huge time during the execution of the algorithm. ECC consists of point doubling and point addition operations. These operations require the execution of many Montgomery modular multiplications that consume time. In this study, we introduce a technique to speed up ECC operations in order to enhance the efficiency of Nasreldin et al. protocol. In particular, we propose a multi-stage parallel design which consists of three stages. First, we speed up the point doubling and point addition operation. Secondly, we enhance the execution time of Montgomery multiplications. Finally, pipelining is used to obtain a better performance. The results show that the proposed design enhances Nasreldin et al. protocol’s execution time by 47.1, 64.7, 73.5 and 79.4%, assuming that the number of nodes is 2, 4, 6 and 12, respectively.
© 2018 Ghada Elkabbany, Mohamed Rasslan and Heba Aslan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.