PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to Indian Application No. 201921009120, filed on Mar. 8, 2019. The entire contents of the aforementioned application are incorporated herein by reference.

TECHNICAL FIELD

The disclosure herein generally relates to machine learning models generation for analytics, and, more particularly, to immutable machine learning models generation for analytics.

BACKGROUND

With an advent of technologies, there is increase in demand for data for analytics. Machine Learning (ML) models are deployed for such data analytics. However, it is realized that there is growing trends of recognition that machine learning models expose new vulnerabilities in software systems. For example, few of current challenges that remain unresolved as dominating threat vectors include training data poisoning, adversarial responses, model extraction, and the like. The exposure of the vulnerabilities are restricted or limited from the above threat vectors. This may be due to large attack surface exposed by ML algorithms because they were designed for deployment in benign environments (e.g., a non-hostile location protected from external hostile elements by physical, personnel, and procedural security countermeasures) as exemplified by the IID (in probability theory and statistics, a sequence or collection of random variables is independent and identically distributed (i.i.d. or iid or IID) if each random variable has the same probability distribution as the others and all are mutually independent) assumption for training and test data. Second reason being limited availability of theoretical tools to analyze generalization and lack of reliable confidence estimates.

Moreover, ML models require data from IoT sources for training different models to derive actionable insight from the data wherein sanctity check needs to performed on the data prior to performing any analytics. However, in reality, data captured from the sensors may not be normalized if the sensor is having noise or the operating condition is beyond the normal operating limits. Existing systems and methods do not have such capabilities to removal noise and as such traceability of the actual data from the source till usage, as the training data set is not captured or derived across the complete process flow. Further, any modification in training dataset for the machine learning model may not guarantee that the end result is achieved without any error thus leading to inaccurate analytics.

SUMMARY

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a processor implemented method for generating immutable machine learning models for data analytics is provided. The method comprises receiving, via one or more hardware processors, an input source data corresponding to at least one trusted data source amongst a plurality of trusted data sources; storing the input source data in a multiple validator node distributed ledger to obtain a plurality of immutable records, wherein the multiple validator node distributed ledger is created for storing the input source data based on at least one of a consensus algorithm and a smart contract between the plurality of trusted data sources; generating one or more machine learning models using the plurality of immutable records retrieved from the multiple validator node distributed ledger; storing the one or more machine learning models as one or more immutable machine learning models in the multiple validator node distributed ledger, wherein the one or more immutable machine learning models are generated based on the plurality of immutable records for one or more passive supervised machine learning techniques; and dynamically triggering, one or more actions specific to at least one Internet of Things (IoT) device deployed in an IoT platform using the one or more immutable machine learning models based on an input request received corresponding to the at least one Internet of Things (IoT) device.

In an embodiment, the input request is received based on a trigger induced by at least one of a rule being executed or an event occurred in the IoT platform.

In an embodiment, the one or more actions comprise activation or deactivation of one or more functionalities of the at least one IoT device based on at least one of a rule being executed or an event occurred in the IoT platform.

In an embodiment, the method may further comprise receiving a request from one or more trusted data sources; anonymizing the plurality of immutable records to obtain a set of anonymization data; and dynamically retrieving from the multiple validator node distributed ledger and providing, at least one of (i) at least a subset of the set of anonymization data, and the one or more immutable machine learning models to the one or more trusted data sources, based on the request.

In another aspect, a system for generating immutable machine learning models for data analytics is provided. The system comprises a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: receive an input source data corresponding to at least one trusted data source amongst a plurality of trusted data sources; store the input source data in a multiple validator node distributed ledger to obtain a plurality of immutable records, wherein the multiple validator node distributed ledger is created for storing the input source data based on at least one of a consensus algorithm and a smart contract between the plurality of trusted data sources; generate one or more machine learning models using the plurality of immutable records retrieved from the multiple validator node distributed ledger; storing the one or more machine learning models as one or more immutable machine learning models in the multiple validator node distributed ledger, wherein the one or more immutable machine learning models are generated based on the plurality of immutable records for one or more passive supervised machine learning techniques; and dynamically trigger, one or more actions specific to at least one Internet of Things (IoT) device deployed in an IoT platform using the one or more immutable machine learning models based on an input request received corresponding to the at least one Internet of Things (IoT) device.

In an embodiment, the input request is received based on a trigger induced by at least one of a rule being executed or an event occurred in the IoT platform.

In an embodiment, the one or more actions comprise activation or deactivation of one or more functionalities of the at least one IoT device based on at least one of a rule being executed or an event occurred in the IoT platform.

In an embodiment, the one or more hardware processors are further configured by the instructions to: receiving a request from one or more trusted data sources; anonymizing the plurality of immutable records to obtain a set of anonymization data; and dynamically retrieving from the multiple validator node distributed ledger and providing, at least one of (i) at least a subset of the set of anonymization data, and the one or more immutable machine learning models to the one or more trusted data sources, based on the request.

In yet another aspect, there are provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause generating immutable machine learning models for data analytics by receiving, via the one or more hardware processors, an input source data corresponding to at least one trusted data source amongst a plurality of trusted data sources; storing the input source data in a multiple validator node distributed ledger to obtain a plurality of immutable records, wherein the multiple validator node distributed ledger is created for storing the input source data based on at least one of a consensus algorithm and a smart contract between the plurality of trusted data sources; generating one or more machine learning models using the plurality of immutable records retrieved from the multiple validator node distributed ledger; storing the one or more machine learning models as one or more immutable machine learning models in the multiple validator node distributed ledger, wherein the one or more immutable machine learning models are generated based on the plurality of immutable records for one or more passive supervised machine learning techniques; and dynamically triggering, one or more actions specific to at least one Internet of Things (IoT) device deployed in an IoT platform using the one or more immutable machine learning models based on an input request received corresponding to the at least one Internet of Things (IoT) device.

In an embodiment, the input request is received based on a trigger induced by at least one of a rule being executed or an event occurred in the IoT platform.

In an embodiment, the one or more actions comprise activation or deactivation of one or more functionalities of the at least one IoT device based on at least one of a rule being executed or an event occurred in the IoT platform.

In an embodiment, the instructions which when executed by the one or more hardware processors further cause receiving a request from one or more trusted data sources; anonymizing the plurality of immutable records to obtain a set of anonymization data; and dynamically retrieving from the multiple validator node distributed ledger and providing, at least one of (i) at least a subset of the set of anonymization data, and the one or more immutable machine learning models to the one or more trusted data sources, based on the request.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

