A Deep Learning Based Privacy Preservation Mechanism For Iot And Iomt Applications

Abstract

Batch Processing In Deep Learning Has Been Explored Extensively To Secure IoMT Networks. Devices Such As Multimedia Sensor Nodes (MSNs) In The IoMT Are Able To Produce Both Multimedia And Non-multimedia Data. The Generated Data Are Sent From A Base Station (BS) To A Cloud Server. However, It's Conceivable That The BS And Cloud Server's Internet Connection Will Be Temporarily Unavailable. The MSNs Are Unable To Store The Acquired Data For A Prolonged Period Of Time Due To The Restricted Computational Capacity. In This Case, MSN Data Can Be Collected By Mobile Devices And Uploaded To A Cloud Server. However, This Data Collection Could Raise Privacy Concerns, Such As Disclosing The Identity And Whereabouts Of MSN Users. Thus, When Collecting And Analyzing Such Sporadic Data From MSNs, It Becomes Vital To Address The Issue Of Data Privacy. The Article Reviews Earlier Research In The Field Of Privacy-preserving Architecture For Certain IoMT Applications. This Paper Presents A Data Collection Mechanism And Neural Batch Processing Approach For Security Of IoMT Applications. It Has Been Shown That The Proposed Work Attains Better Performance Compared To Existing Baseline Techniques.

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