Abstract: With the growing use of large-scale datasets containing participant's genomics and clinical data for research and studies purposes, it is important to ensure the privacy of the participant by generating a secure representation of genome data observation. Embodiments herein provide a system and method to perform DNA analysis on an encrypted genome without the need for decryption and ensure user's genome privacy. Herein, a stream of input genome is divided into chunks called as the sketched, where each k-mer in the sketch is hashed to get a sampling level and a counter value for the k-mer in a sketch. A privacy enhancing technique is designed over the representations using a Fully Homomorphic Encryption (FHE). Genomic data is analyzed with identified efficient algorithms for real world deployment in encrypted domain.

Abstract: Malicious website detection has been very crucial in timely manner to avoid phishing. User privacy also needs to be maintained at the same time. A system and method for classifying a website URL have been provided. The system is configured to achieve end-to-end privacy for machine learning based malicious URL detection. The system provides privacy preserving malicious URL detection models based on Fully Homomorphic Encryption (FHE) approach either using deep neural network (DNN), using logistic regression or using a hybrid approach of both. The system is utilizing a split architecture (client-server) where-in feature extraction is done by a client machine and classification is done by a server. The client machine encrypts the query using FHE and sends it to the server which hosts machine learning model. During this process, the server doesn't learn any information about the query.

Abstract: Current solutions in literature use Zero Knowledge Proofs (ZKP) to ensure cryptographic guarantees for account balances. These are highly complex and require large memory. Hence there is a need for a computationally efficient mechanism that can preserve the privacy of customer financial information and provides verifiable cryptographic guarantees for debits and credits of a transaction without revealing customer information. Embodiments of the present disclosure provide a method and system for an inter-bank transaction with privacy enabled auditing and a privacy enabled inter-bank settlements in blockchain network. The method disclosed leverages cryptographic primitives such as Boneh-Lynn-Shacham (BLS) to preserve the privacy of the customer's financial information and to enable faster auditable settlement between the banks in the presence of a governing body.

Abstract: The disclosure herein generally relates to the field of privacy preserving in an application, and, more particularly, to enabling privacy in an application using fully homomorphic encryption. The disclosure more specifically refers to enabling a most optimal FHE for privacy preserving for the application based on a set of constraints using a disclosed set of optimization tasks. The set of optimization tasks comprise a multi objective-multi constraint optimization task and a single objective-multi constraint optimization task, that identifies an optimal FHE library, along with an associated FHE functionality and an optimal configuration of the associated FHE functionality based on the set of constraints. The identified FHE library along with the associated FHE functionality and the optimal configuration of the associated FHE functionality facilitate optimal implementation of privacy in the applications.

Abstract: Authentication is a key procedure in information systems. Conventional biometric authentication system is based on a trusted third-party server which is not secure. The present disclosure provides a privacy preserving multifactor biometric authentication for authenticating a client without the third-party authentication server. The server receives a plurality of encrypted biometric features from the client, encrypted using Fully Homomorphic Encryption. Further, the server evaluates the plurality of encrypted biometric features to obtain a client identifier value and a plurality of encrypted resultant values. The server encrypts each of the plurality of resultant values based on a time based nonce and the client identifier value. The encrypted authentication tags and the corresponding resultant values are aggregated by the server and transmitted to the client. The client decrypts the resultant value and the authentication tag and transmits to the server.

Abstract: Authentication is a key procedure in information systems. Conventional biometric authentication system is based on a trusted third-party server which is not secure. The present disclosure provides a privacy preserving multifactor biometric authentication for authenticating a client without the third-party authentication server. The server receives a plurality of encrypted biometric features from the client, encrypted using Fully Homomorphic Encryption. Further, the server evaluates the plurality of encrypted biometric features to obtain a client identifier value and a plurality of encrypted resultant values. The server encrypts each of the plurality of resultant values based on a time based nonce and the client identifier value. The encrypted authentication tags and the corresponding resultant values are aggregated by the server and transmitted to the client. The client decrypts the resultant value and the authentication tag and transmits to the server.

Abstract: The disclosure herein generally relates to the field of privacy preserving in an application, and, more particularly, to enabling privacy in an application using fully homomorphic encryption. The disclosure more specifically refers to enabling a most optimal FHE for privacy preserving for the application based on a set of constraints using a disclosed set of optimization tasks. The set of optimization tasks comprise a multi objective-multi constraint optimization task and a single objective-multi constraint optimization task, that identifies an optimal FHE library, along with an associated FHE functionality and an optimal configuration of the associated FHE functionality based on the set of constraints. The identified FHE library along with the associated FHE functionality and the optimal configuration of the associated FHE functionality facilitate optimal implementation of privacy in the applications.

Abstract: Malicious website detection has been very crucial in timely manner to avoid phishing. User privacy also needs to be maintained at the same time. A system and method for classifying a website URL have been provided. The system is configured to achieve end-to-end privacy for machine learning based malicious URL detection. The system provides privacy preserving malicious URL detection models based on Fully Homomorphic Encryption (FHE) approach either using deep neural network (DNN), using logistic regression or using a hybrid approach of both. The system is utilizing a split architecture (client-server) where-in feature extraction is done by a client machine and classification is done by a server. The client machine encrypts the query using FHE and sends it to the server which hosts machine learning model. During this process, the server doesn't learn any information about the query.