Downloading: Distributed Data Provenance (DDP) to Secure the Sensor Data Stream
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
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Research Paper | Computer Science & Engineering | China | Volume 8 Issue 6, June 2019

Distributed Data Provenance (DDP) to Secure the Sensor Data Stream

Mohammad Amanul Islam

Introducing provenance has already been established as a very substantial approach of securing the sensor data that records the history (e.g., creation, ownership, significance) and operations performed on the data or its travel path. The data provenance is also crucial for ensuring data accountability and privacy as preserving the quality of data stored in numerous systems. Hence, keeping the provenance information requires to address several challenges including storage overhead, high throughput, and secure transmission, etc. In this paper, we propose an architecture of distributed data provenance that avoids the problem of data degradation because of adding a large size of data provenance. Moreover, it enables the transparency of data accountability, helps to enhance the privacy and availability of data provenance. In this scheme, the provenance signifies the creation history of data in the hash form of data, and it has been distributed as chained information with the data into the given data payload of the data packet. Experiments demonstrate the efficiency, scalability on the provenance encoding capacity of the proposed scheme, and the reliability of this scheme has been illustrated with low transmission overhead for the payload storage application of a packet in the wireless sensor environment.

Keywords: bits, data, provenance, payload, wireless sensor network

Edition: Volume 8 Issue 6, June 2019

Pages: 410 - 419

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How to Cite this Article?

Mohammad Amanul Islam, "Distributed Data Provenance (DDP) to Secure the Sensor Data Stream", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20198177, Volume 8 Issue 6, June 2019, 410 - 419



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