Osint By Crowdsourcing: A Theoretical Model For Online Child Abuse Investigations
Kemal Veli Açar · Zenodo (CERN European Organization for Nuclear Research) · 2018
For various reasons, the backlog of seized devices has increased to unparalleled levels, sometimes leading to years of delays in the trial process, even for online child abuse investigations. In addition to child abuse materials, the digital belongings of an abuser might contain small yet important pieces of information, such as nicknames, e-mails and place names. A thorough digital examination and an appropriate analysis of this set of information might reveal the exact locations or the real identities of criminal associates or victimized children. All these digital clues should be investigated properly through open sources, to identify the real identities of possible owners and discover whether their relationships with the suspect are crime-related. Considering the fact that thousands of such digital traces are present in all seized material for each separate investigation, a thorough examination of every piece of information related to a criminal case becomes a tremendously challenging issue for law enforcement agencies (LEAs). While the resources of LEAs are clearly insufficient, automated methods alone are not adequate to respond fully to current needs. In this regard, this article proposes a new and unorthodox way of handling the ever-growing workload of online child abuse investigators more effectively, by tapping into the energy of a carefully selected crowd of volunteers. After a brief literature review on related subjects, such as open source intelligence (OSINT) and crowdsourcing, in terms of their technical, legal and organizational aspects, the proposed theoretical model will be elaborated. Likely concerns and probable bottlenecks relating to the same respective aspects regarding successful actualization of the model will be identified and thoroughly discussed.