Achieving trust in cloud computing using secure data provenance

Abstract

Cloud computing is the next generation computer system which extends network architecture into dynamic and large scale capacity by using visualization techniques. Transparent and secure data provenance will enhance the level of trust in the Cloud. Recent literature on securing data provenance focuses only on providing partial part of security elements in their mechanisms, thus they could not provide full protection to the data provenance as a whole. This paper presents the provenance description and challenges in providing security assurances in the Cloud. The paper also proposes a novel trust model for data provenance in cloud computing. The model will be used for securing the transaction process of storing and accessing the data provenance. The paper describes in detail the flow process of the model to achieve a high level of trust in cloud services. The significance of this research is to enhance the level of trust in the cloud services via comprehensive trust model which consists all aspects of security elements in the model.

Implementation of Link Fingerprint Verification and Data Provenance using RSSI

“…The accumulation and access of data provenance are additionally essential to be a verified procedure. The proposed trust model is portrayed for cloud computing in [6]. High trust can be accomplished utilizing a similar model in IoT condition.…”

“…The data provenance can be imitated or altered by an unapproved party if the provenance isn't appropriately ensured by executing unproductive security methods. So as to build up the trust of IoT, a solution for security ought to be planned which is light-weight and profoundly verified [6]. The majority of the security algorithms and cryptography procedures utilized today comprises of huge computational complexity with heightened energy utilization.…”

An Approach for Estimating Forensic Data Provenance of an Object in the Cloud Environment using One Dimensional Successive Bisection Method

“…Based on experiments conducted using objects {A,B} and {A,C}, {D,E}, {F,G}, {F,H} the solution converges to different figures when [1,2], [2,3], [2,4], [2,5], [2,6] root interval are used. Table 3 shows the convergence and functional values of the experiments …”

“…The testing experiments have generated five different scenarios for the objects {A,B} and {A,C}, {D,E}, {F,G}, {F,H} and the test values [1,2], [2,3], [2,4], [2,5] and [2,6] respectively. The authors chose to carry out these separate tests for two main reasons: To find the convergence of the roots that are treated as data objects that move within the cloud and to find the functional object that will help to show how closeness the objects can be during data provenance.…”

“…According to Reddy and Seltzer [1] it is metadata that provides details of the origins or history of a data object" which describes people, entities and activities that were involved in producing a data object. Nevertheless, a lot of care needs to be taken when tracking data provenance because according to Saad, Jalil and Manaf [2], there can exist provenance integrity if there is false information or tampering by unauthorized party.…”