TECHNICAL FIELD

This disclosure relates generally to encrypting data, and more particularly to a method and system for selectively encrypting a dataset.

BACKGROUND

In recent times, there have been multifarious application of Artificial Intelligence (AI) models, for example, deep learning models. Due to such increased usage and high complexity, these AI models are stored over cloud or in a server. Users may use these stored AI models as a service, so as to perform predefined actions. As it may be appreciated, various deep learning models may be generated, with each model having a different specification in terms of speed, accuracy, false positives, etc. Accordingly, the user can select a model as per his/her requirement to perform predefined actions. User data is processed using the selected model, and results are obtained for further processing.

However, these deep learning models stored over the cloud or in the server are prone to vulnerabilities and security attacks. For example, a hacker may alter weights of the deep learning model, thereby influencing the decisions obtained using the model. Further, feeding user data into the model is risky as this may expose the data to third parties. Moreover, most users do not prefer to store their sensitive data, such as salary details, loan amounts, etc., on the cloud while using the services of the deep learning models. For example, a classifier model (deep learning model) stored on a cloud may be capable of providing a response to a user on whether the user is eligible for a loan or not, based on a required loan amount, previous records of payment, salary details of the user, other outstanding loan amount, age of the user, etc. In this example, financial institutions (banks) are often reluctant to send these personal details of users over the internet to access the model.

Conventionally, a user may send their data to a model over a cloud upon encrypting the data. Accordingly, results obtained from the model need to be decrypted at the user end, using homomorphic encryption techniques. Although, the conventional homomorphic encryption techniques can be used. However, these techniques require complex keys involving complex operations. Moreover, in some cases, stronger keys are used to protect a simple and obvious data. This results in unnecessary computations, and consequently power loss and operational delays.

SUMMARY

In one embodiment, a method of selective encryption of a test dataset is disclosed. The method may include determining a relevancy grade associated with each of a plurality of datapoints within a test dataset by comparing the test dataset with a common heat map. The common heat map is generated using a plurality of training datasets. The method may further include calculating, based on the relevancy grade, an encryption level associated with each of the plurality of datapoints. The method may further include selectively encrypting at least one datapoint from the plurality of datapoints based on the encryption level associated with each of the plurality of datapoints. The at least one data point is rendered to a user after being decrypted.

In another embodiment, an encryption device for selective encryption of a test dataset is disclosed. The encryption device includes a processor and a memory communicatively coupled to the processor. The memory stores processor instructions, which, on execution, may cause the processor to determine a relevancy grade associated with each of a plurality of datapoints within a test dataset by comparing the test dataset with a common heat map. The common heat map is generated using a plurality of training datasets. The processor instructions, on execution, may further cause the processor to calculate, based on the relevancy grade, an encryption level associated with each of the plurality of datapoints. The processor instructions, on execution, may further cause the processor to selectively encrypt at least one datapoint from the plurality of datapoints based on the encryption level associated with each of the plurality of datapoints. The at least one data point is rendered to a user after being decrypted.

In yet another embodiment, a non-transitory computer-readable storage medium is disclosed. The non-transitory computer-readable storage medium has stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps including determining a relevancy grade associated with each of a plurality of datapoints within a test dataset by comparing the test dataset with a common heat map, wherein the common heat map is generated using a plurality of training datasets; calculating, based on the relevancy grade, an encryption level associated with each of the plurality of datapoints; and selectively encrypting at least one datapoint from the plurality of datapoints based on the encryption level associated with each of the plurality of datapoints. The at least one data point is rendered to a user after being decrypted.

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.

