Data has become an indispensable part of our daily lives in this era of information age. The amount of data which is generated is growing exponentially due to technological advances. This voluminous of data which is generated daily has brought about new term which is referred to as big data. Therefore, security is of great concern when it comes to securing big data processes. The survival of many organizations depends on the preventing of these data from falling into wrong hands. Because if these sensitive data fall into wrong hands it could cause serious consequences. For instance, the credibility of several businesses or organizations will be compromised when sensitive data such as trade secrets, project documents, and customer profiles are leaked to their competitors (Alneyadi et al, 2016). In addition, the traditional security mechanisms such as firewalls, virtual private networks (VPNs), and intrusion detection systems/intrusion prevention systems (IDSs/IPSs) are not enough to prevent against the leakage of such sensitive data. Therefore, to overcome this deficiency in protecting sensitive data, a new paradigm shift called data leakage prevention systems (DLPSs) have been introduced. Over the past years, many research contributions have been made to address data leakage. However, most of the past research focused on data leakage detection instead of preventing against the leakage. This thesis contributes to research by using the preventive approach of DLPS to propose hybrid symmetric-asymmetric encryption to prevent against data leakage. Also, this thesis followed the Design Science Research Methodology (DSRM) with CRISP-DM (CRoss Industry Standard Process for Data Mining) as the kernel theory or framework for the designing of the IT artifact (method). The proposed encryption method ensures that all confidential or sensitive documents of an organization are encrypted so that only users with access to the decrypting keys can have access. This is achieved after the documents have been classified into confidential and non-confidential ones with Naïve Bayes Classifier (NBC). Therefore, any organizations that need to prevent against data leakage before the leakage occurs can make use of this proposed hybrid encryption method.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-69685 |
| Date | January 2018 |
| Creators | Nyarko, Richard |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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