Optimization of Mobile Forensics using Efficient Data Deduplication

Abstract

The data on mobile devices provides crucial information for criminal investigation. Hence, mobile devices have become a focus point for forensic investigation, with smartphones dominating the worldwide crime scene. The size of data or information becomes an obstacle as the extensive use of digital phones has drastically increased. This problem leads to the need for adequate storage management. To streamline and effectively utilize smartphone storage systems, redundant data must be carefully examined and removed from the device. This can be solved with the help of a technique called data deduplication. Deduplication improves device speed and aids digital forensics investigations by eliminating duplicate data and decreasing storage requirements for forensics analysis purposes. The majority of the deduplication solutions primarily concentrate on cloud environments, that overlooks the specific challenges mobile devices pose, and includes limited processing power, storage space, network bandwidth, security considerations, data fragmentation, and dynamic data. However, the data deduplication solution increases storage requirements for metadata, processing delays, and accuracy. It also affects evidence integrity and increases the potential risk of data leaking. newline newlineThe main objective of this work is to introduce an improved data deduplication technique designed exclusively for Android smartphones, tackling the distinct obstacles that Law Enforcement Agencies (LEAs) face during forensic inquiries. Various strategies have been tested to provide better outcomes, and a comparative analysis of the optimization of each approach has been provided. The proposed solutions use a file-level deduplication technique to consider resource-constrained devices that have less computing power and memory. The experimentation analysis demonstrates that the proposed solutions minimize file analysis time by minimizing processing time, saving storage space, and utilizing less RAM. Battery consumption has also been reduced significantly. Overhead