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In recent times, amount of data stored in the smartphones have increased phenomenally. A smartphone is as powerful as a laptop or a desktop where people store their person data or do daily activities, as a result it can act as an important evidence for law enforcement while solving the cases such as, in case of accident, malicious exchange of text messages, photos or videos taken during mass shooting incident. This act as an important forensic interest to the investigator. Some people may be willing to give their phones to the investigator, but they would like to make sure that their privacy and their data privacy have been taken into consideration, meaning that only data relevant to the case under investigation should be analyzed and collected. Even supreme court have passed that ruling to preserve the users and data privacy. In this research study; a new forensic tool is developed which can do selective extraction of data from an android device. The input to this tool is based on the consent form which is filled by the witness/victim who voluntarily hands over his/her phone to law enforcement and investigator extracts data within those limits.
This tool does the extraction on metadata and content based filtering and export the extracted data along with the hash values to a bootable drive in a forensically sound manner. State-of the art machine learning models are used to perform content based filtering. As a result, a robust and efficient tool is built to solve the real time cases while preserving the users and data privacy.



Android forensics, Selective extraction, Machine learning, Tensorflow, Privacy, Data acquisition