Security Systems with Biometry based on Partial View Facial Images Using Geometrical Features

Milczarski, Piotr; Stawska, Zofia; Dowdall, Shane

doi:10.1109/idaacs-sws.2018.8525678

Cited by 1 publication

(1 citation statement)

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“…However, these protocols also introduce some communication costs and may require some level of coordination between untrusting parties. [18]- [20] Zero-knowledge proofs are cryptographic procedures which let one party to prove to the other party that he knows particular knowledge without need to reveal the knowledge items. It allows performing authentication and verification procedures without the need to transmit any sensitive data.…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream

Prabhat Kumar

2024

jes

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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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Section: Literature Reviewmentioning

confidence: 99%

Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream

Prabhat Kumar

2024

jes

View full text Add to dashboard Cite

show abstract

Security Systems with Biometry based on Partial View Facial Images Using Geometrical Features

Cited by 1 publication

References 16 publications

Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream

Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream

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