US-20260127306-A1 - SYSTEMS AND METHODS OF FACILITATING SECURE INFERENCING OF A MACHINE LEARNING MODEL

US20260127306A1US 20260127306 A1US20260127306 A1US 20260127306A1US-20260127306-A1

Abstract

The present disclosure provides the method of facilitating secure inferencing of a machine learning model. Further, the method may include receiving an input data from a client device associated with a client. Further, the method may include analyzing the input data. Further, the method may include receiving a relevant data from a data source device associated with a data source. Further, the receiving of the relevant data may be based on the analysis. Further, the method may include processing the relevant data in accordance with the machine learning model to obtain a processed relevant data. Further, the machine learning model may be immutable. Further, the method may include generating an output data using the machine learning model. Further, the generating of the output data may be based on the processed relevant data and the input data. Further, the method may include transmitting the output data to the client device.

Inventors

Elad RAVE

Assignees

Elad RAVE

Dates

Publication Date: 20260507
Application Date: 20241106

Claims (20)

1 . A method of facilitating secure inferencing of a machine learning model, the method comprising: receiving, using the communication device, an input data from a client device associated with a client; analyzing, using a processing device, the input data; receiving, using the communication device, a relevant data from a data source device associated with a data source, wherein the receiving of the relevant data is based on the analysis; processing, using the processing device, the relevant data in accordance with the machine learning model to obtain a processed relevant data, wherein the machine learning model is immutable; generating, using the processing device, an output data using the machine learning model, wherein the generating of the output data is based on the processed relevant data and the input data; and transmitting, using the communication device, the output data to the client device.
2 . The method of claim 1 , wherein the input data comprises a user identifier, wherein the processed relevant data is associated with an access level indicator corresponding to the user identifier, wherein the generating of the output data is based on the access level indicator.
3 . The method of claim 1 , wherein the processing of the relevant data comprises: generating, using the processing device, an adapter; integrating, using the processing device, the adapter into at least one layer of the machine learning model.
4 . The method of claim 1 further comprising: receiving, using the communication device, an access request from the client device, wherein the access request is associated with one or more of transmitting and receiving of one or more of the input data, the relevant data, and the output data; authenticating, using the processing device, the client device based on the access request and an access criterion, wherein one or more of transmitting, using the communication device, of one or more of the input data, the relevant data, and the output data to the client device and receiving, using the communication device, of one or more of the input data, the relevant data and the output data is based on the authentication.
5 . The method of claim 1 , wherein the processing of the relevant data comprises altering the relevant data using an auxiliary machine learning model configured to alter the relevant data to generate the processed relevant data.
6 . The method of claim 1 , wherein the processing of the relevant data comprises altering the relevant data using an adapter configured to alter the relevant data to generate the processed relevant data based on the machine learning model.
7 . The method of claim 6 , wherein the adapter comprises a plurality of adapters comprising a first adapter and a second adapter, wherein the relevant data is input to the first adapter, wherein the processing of the relevant data comprises generating, using the first adapter, a first intermediate output data, wherein the first intermediate output data is input to the second adapter, wherein the processing of the relevant data further comprises generating, using the second adapter, a second intermediate output data based on the first intermediate output data, wherein generation of the processed relevant data is based on the second intermediate output data.
8 . The method of claim 6 , wherein the adapter comprises a plurality of adapters comprising a first adapter and a second adapter, wherein the relevant data is input to the first adapter, wherein the processing of the relevant data comprises generating, using the first adapter, a first intermediate output data, wherein the relevant data is input to the second adapter, wherein the processing of the relevant data further comprises generating, using the second adapter, a second intermediate output data, wherein generation of the processed relevant data is based on the first intermediate output data, the second intermediate output data.
9 . The method of claim 1 further comprises receiving, using the communication device, the machine learning model from the client device.
10 . The method of claim 1 further comprising receiving, using the communication device, a control data from the client device, wherein the method further comprising storing, using a storage device, the processed relevant data based on the control data.
11 . A system for facilitating secure inferencing of a machine learning model, the system comprising: a communication device configured to: receive an input data from a client device associated with a client; receive a relevant data from a data source device associated with a data source, wherein the receiving of the relevant data is based on an analysis; transmit an output data to the client device; a processing device communicatively coupled with the communication device, wherein the processing device is configured to: analyze the input data; process the relevant data in accordance with the machine learning model to obtain a processed relevant data, wherein the machine learning model is immutable; and generate the output data using the machine learning model, wherein the generating of the output data is based on the processed relevant data and the input data.
12 . The system of claim 11 , wherein the input data comprises a user identifier, wherein the processed relevant data is associated with an access level indicator corresponding to the user identifier, wherein the generating of the output data is based on the access level indicator.
13 . The system of claim 11 , wherein the processing of the relevant data comprises configuring the processing device to: generate an adapter; and integrate the adapter into at least one layer of the machine learning model.
14 . The system of claim 11 , wherein the communication device is further configured to receive an access request from the client device, wherein the access request is associated with one or more of transmitting and receiving of one or more of the input data, the relevant data and the output data, wherein the processing device is further configured to authenticate the client device based on the access request and an access criterion, wherein one or more of transmitting, using the communication device, of one or more of the input data, the relevant data and the output data to the client device and receiving, using the communication device, of one or more of the input data, the relevant data and the output data is based on the authentication.
15 . The system of claim 11 , wherein the processing of the relevant data comprises altering the relevant data using an auxiliary machine learning model configured to alter the relevant data to generate the processed relevant data.
16 . The system of claim 11 , wherein the processing of the relevant data comprises altering the relevant data using an adapter configured to alter the relevant data to generate the processed relevant data based on the machine learning model.
17 . The system of claim 16 , wherein the adapter comprises a plurality of adapters comprising a first adapter and a second adapter, wherein the relevant data is input to the first adapter, wherein the processing of the relevant data comprises generating, using the first adapter, a first intermediate output data, wherein the first intermediate output data is input to the second adapter, wherein the processing of the relevant data further comprises generating, using the second adapter, a second intermediate output data based on the first intermediate output data, wherein generation of the processed relevant data is based on the second intermediate output data.
18 . The system of claim 16 , wherein the adapter comprises a plurality of adapters comprising a first adapter and a second adapter, wherein the relevant data is input to the first adapter, wherein the processing of the relevant data comprises generating, using the first adapter, a first intermediate output data, wherein the relevant data is input to the second adapter, wherein the processing of the relevant data further comprises generating, using the second adapter, a second intermediate output data, wherein generation of the processed relevant data is based on the first intermediate output data, the second intermediate output data.
19 . The system of claim 11 , wherein the communication device is further configured to receive the machine learning model from the client device.
20 . The system of claim 11 , wherein the communication device is further configured to receive a control data from the client device, wherein the method further comprising storing, using a storage device, the processed relevant data based on the control data.

Description

FIELD OF DISCLOSURE Generally, the present disclosure relates to the field of data processing. More specifically, the present disclosure relates to methods and systems for facilitating secure inferencing of a machine learning model. BACKGROUND With the increasing reliance on AI models in enterprise applications, it is crucial to ensure the security, privacy, and integrity of these models. Traditional methods often risk information leakage and unauthorized access, compromising both data and model security. The present disclosed system addresses these issues by providing a secure, privacy-preserving, and zero-trust framework for AI model inferencing and management. SUMMARY OF DISCLOSURE This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter. Nor is this summary intended to be used to limit the claimed subject matter's scope. The present disclosure provides the method of facilitating secure inferencing of a machine learning model. Further, the method may include receiving, using the communication device, an input data from a client device associated with a client. Further, the method may include analyzing, using a processing device, the input data. Further, the method may include receiving, using the communication device, a relevant data from a data source device associated with a data source. Further, the receiving of the relevant data may be based on the analysis. Further, the method may include processing, using the processing device, the relevant data in accordance with the machine learning model to obtain a processed relevant data. Further, the machine learning model may be immutable. Further, the method may include generating, using the processing device, an output data using the machine learning model. Further, the generating of the output data may be based on the processed relevant data and the input data. Further, the method may include transmitting, using the communication device, the output data to the client device. The present disclosure provides the system of facilitating secure inferencing of a machine learning model. Further, the system may include a communication device. Further, the communication device may be configured to receive an input data from a client device associated with a client. Further, the communication device may be configured to receive a relevant data from a data source device associated with a data source. Further, the receiving of the relevant data may be based on an analysis. Further, the communication device may be configured to transmit an output data to the client device. Further, the system may include a processing device communicatively coupled with the communication device. Further, the processing device may be configured to analyze the input data. Further, the processing device may be configured to process the relevant data in accordance with the machine learning model to obtain a processed relevant data. Further, the machine learning model may be immutable. Further, the processing device may be configured to generate the output data using the machine learning model. Further, the generating of the output data may be based on the processed relevant data and the input data. Both the foregoing summary and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing summary and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description. BRIEF DESCRIPTIONS OF DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicants. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the applicants. The applicants retain and reserve all rights in their trademarks and copyrights included herein, and grant permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose. Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure. FIG. 1 is an illustration of an online platform 100 consistent with various embodiments of the present disclosure. FIG. 2 is a