Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream
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Abstract
The proposed privacy-preserving framework based on fog computing for securing IoT data was examined through 10 experiment trials, with each trial dissecting a number of performance related metrics. Specifically, across the trials, latency values varied between 45 and 55 milliseconds, which signified that communication overhead was minute and that data were processed efficiently. Throughput values also varied considerably, yet only between 95 and 110 megabits per second, which signalled that the framework would allow processing data at high speeds. The rates of resource utilization measured in terms of MHR from CPU and Mused in the memory of specific fog nodes, varied between 58% and 76% . Regarding the scalability of the proposed framework, it was assessed based on the data collected and divided into the corresponding categories. From the energy consumption analysis, the values varied between 470 and 530 joules , which was recognized as the change caused by the shifting performance of the proposed IoT solution. Finally, communication overhead values varied from 970 to 1050 bytes, showing the differences in the effects which privacy-preserving frameworks have on data transmission. In conclusion, the results indicate that the proposed complex is immensely efficient in terms of protecting sensitive IoT data, ensuring a high level of security, preserving privacy, maintaining the current performance, and being adjusted to the new threats and security challenges.
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