Abstract: State of the art approaches used to address security aspects in serverless platforms perform workflow validations on an end to end flow, however, this cannot prevent attacks targeted at intermediate function calls in the workflow. Further, the existing systems store policy data in insecure manner, which causes security issues. The disclosure herein generally relates to serverless clouds, and, more particularly, to a method and system for privacy-preserving workflow validations in serverless clouds. The system stores policy data in a secured/encrypted manner. The system also performs validations at different levels, at a first level to allow/deny access at an ingress point, and at a second level to allow/deny access at critical intermediate points. This approach thus provides safety against attacks that may have been initiated post initial validation, and offers added data security.

Abstract: The present disclosure provides a technique to evaluate encrypted Machine Learning (ML) models. Conventional methods are unable to provide a holistic approach to evaluate encrypted ML models. Initially, the system receives an encrypted ML model. The ML model can be an unencrypted ML model trained with encrypted data or an encrypted ML model trained with encrypted data, or an encrypted ML model trained with unencrypted data. Further, a plurality of evaluation functions pertaining to the ML model to be calibrated are identified using a pattern matching technique. Further, an approximated function is generated for each of the plurality of evaluation functions using a corresponding approximation technique. After generating a plurality of approximated functions, an Expected Calibration Error (ECE) value is computed based on the plurality of approximated functions. Finally, the ML model is calibrated based on the computed ECE value. The ML model is perfectly calibrated if the computed ECE value is equal to zero.

Abstract: The present disclosure detects a pupil of an eye using circle formation based scoring method. The conventional approaches fail to provide an accurate and reliable biometric authentication due to the usage of simple thresholding based statistical methods and iris dependent segmentation methods. The present disclosure utilizes a circle plotting approach and selects the optimum circle using several parameters. The present disclosure can generate a pupil boundary that fits the pupil region inside an iris perfectly. Initially, the system receives an input image of an eye. After removing reflections, a core point of the reflection free image is identified. Further, a plurality of points are obtained based on a sudden gradient change. and a plurality of circles are plotted. Further, an optimum circle is identified using a score based optimum circle selection method. Finally, the pupil associated with the input image is identified based on the optimum circle.

Abstract: The disclosure generally relates to scene graph generation. Scene graph captures rich semantic information of an image by representing objects and their relationships as nodes and edges of a graph and has several applications including image retrieval, action recognition, visual question answering, autonomous driving, robotics. However, to leverage scene graphs, computationally efficient scene graph generation methods are required, which is very challenging to generate due presence of a quadratic number of potential edges and computationally intensive/non-scalable techniques for detecting the relationship between each object pair using the traditional approach. The disclosure proposes a combination of edge proposal neural network and the Graph neural network with spatial message passing (GNN-SMP) along with several techniques including a feature extraction technique, object detection technique, un-labelled graph generation technique and a scene graph generation technique to generate scene graphs.

Abstract: State of the art systems used for airport automation and data processing may be prone to data security related issues, as unauthorized personal may gain entry to sensitive data. The disclosure herein generally relates to airport management, and, more particularly, to a method and system for service authentication in an airport management network. The system uses a neural network to process a received service request and decides whether the service request is to be allowed or denied, based on a determined validity of the service request, role based access defined for a user requesting the service, a feature map data generated.

Abstract: This disclosure relates to field of cryptography and digital signatures. The constant theft of cryptocurrencies is due to compromise of the secret signing key inherently stored in a single location. Also, one of challenges in the existing ECDSA signature is single point failure, wherein the signing key (private key) is prone to theft. The disclosed technique overcomes the challenging in the existing techniques by party distributed signature that ensures the safety of the private key. The disclosed techniques slits the key in two parts and saves at two different locations/machines. Further based on a ECDSA based technique, the digital signature is obtained securely using several steps that includes generation of first two parts of digital signature at one party, generation of the second two parts of digital signature at second party, finally decrypting a complete digital signature at the first party.

Abstract: Transaction executions/commits in a blockchain network need to be fast, robust and secure and thus calls for minimal latency in transaction commits. In an execute-order-commit blockchain network, latency is high due to smart contracts been executed at every endorsing node of the blockchain network. A method and system for processing transactions in the blockchain network is disclosed. The system discloses a veriblock architecture, which enables processing a transaction request by executing an associated smart contract along with a proof of correctness of execution of smart contract using only one endorser. Further, enables verifying the smart contract by multiple endorsers. The smart contract associated with the proof, referred herein as a vericontract, is executed to generate an output and the proof using one of a) Verifiable Computing (VC) approach, b) a TEE approach and c) a hybrid approach (combination of VC and TEE).

Abstract: The present disclosure provides an efficient system for cancelable biometric template protection which is not available in the conventional systems. Initially, the system receives a biometric template from a user. The biometric template is one of, a face image, a fingerprint, an iris image and a palmprint. Further, a vector embedding is computed based on the biometric template using a pretrained neural network. After computing the vector embedding, a key based permutation is computed based on the vector embedding and a permutation key. The permutation key is generated based on an Identification number (ID) associated with the user using a random number based key generation technique. Finally, a protected biometric template is generated by computing a permuted embedding corresponding to the biometric template based on the key based permutation using a plurality of fully connected layers pretrained using a weight based center triplet loss function.

Abstract: A method and system for finding vulnerabilities in a program using fuzzing have been provided. The disclosure provides a vulnerability detection framework using a language agnostic single fuzzer that can fuzz smart contracts written in different programming languages. The idea here is that a smart contract written in a high-level language is converted/compiled into an LLVM intermediate representation (LLVM IR) code and then perform the fuzzing on this LLVM IR code instead of fuzzing smart contract source code directly. The process of generating fuzz driver, report driver is automated by handling the standardization issue by carefully dividing the smart contracts into categories. The present disclosure is proposing processes of automation of fuzz or report driver generation. Further the language agnostic feature (done with intermediate representation) is also achieved. Further profiling is achieved which processes fuzzer output and generates meaningful data points.

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: The present disclosure provides a federated learning based identification of non-malicious classification models where the conventional model fails to perform. Initially, the system receives a local classification model from each of a plurality of clients. Further, a set of one-dimensional arrays are obtained based on a plurality of local classification models associated with the plurality of clients using a flattening technique. Further, a major cluster and a minor cluster are obtained by clustering the set of one-dimensional arrays using a clustering technique. After clustering, a plurality of active classification models are selected based on the major cluster and the minor cluster using an epsilon cluster selection technique. Further, a global classification model is selected from the plurality of active models using a random selection technique. Finally, the selected global classification model is transmitted to each of the plurality of clients.

Abstract: Conventionally, biometric template protection has been achieved to improve matching performance with high levels of security by use of deep convolution neural network models. However, such attempts have prominent security limitations mapping information of images to binary codes is stored in an unprotected form. Given this model and access to the stolen protected templates, the adversary can exploit the False Accept Rate (FAR) of the system. Secondly, once the server system is compromised all the users need to be re-enrolled again. Unlike conventional systems and approaches, present disclosure provides systems and methods that implement encrypted deep neural network(s) for biometric template protection for enrollment and verification wherein the encrypted deep neural network(s) is utilized for mapping feature vectors to a randomly generated binary code and a deep neural network model learnt is encrypted thus achieving security and privacy for data protection.

Abstract: This disclosure relates generally to a method and system for biometric verification. Conventional biometric verification method and system performs one or more computations in non-encrypted domain, thereby leading to security threats. The disclosed method includes performing computations such as enrollment and verification feature vector computation, dimensionality reduction of said feature vectors, and comparison of dimensionally reduced encrypted feature vectors to obtain matching scores indicating the extent of match therebetween between in encrypted domain using fully homomorphic encryption, thereby leading to secure biometric verification.

Abstract: This disclosure relates generally to method and system for securing peer nodes in a blockchain network. The proposed disclosure is a robust model providing secure, scalable and efficient sharding committee reconfiguration technique where blockchain peer nodes organize themselves into each sharding committee among a plurality of sharding committees. The disclosure includes, generating a random number directory by each peer node communicating random numbers to the reference committee through leader node in the blockchain network. The reference committee initiates to reconfigure members of each sharding committee at predefined intervals. Further, a first message packet from each peer node is received by the reference committee based on which a second message packet is generated enabling each peer node of the block chain network to join one of the sharding committee.

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: This disclosure relates generally to contract management, and more particularly to contract management in a data marketplace. In an embodiment, a system for contract management performs refactoring of a contract, during which the system extracts terms and conditions from the contract and generates a simplified view of the contract. The system further performs a requirement validation based on the contract, during which the system determines features of data entity matches requirements specified by a first party or not, based on domain specific ontologies. If the data entity features are not matching with the requirements, then the system fetches one or more relevant attributes from a list of ontologies, verifies whether the features of entity along with the selected feature(s) satisfy the requirements or not. The system accordingly generates an agreeable requirement document as output of the requirement validation.

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: This disclosure relates generally to method and system for securing peer nodes in a blockchain network. The proposed disclosure is a robust model providing secure, scalable and efficient sharding committee reconfiguration technique where blockchain peer nodes organize themselves into each sharding committee among a plurality of sharding committees. The disclosure includes, generating a random number directory by each peer node communicating random numbers to the reference committee through leader node in the blockchain network. The reference committee initiates to reconfigure members of each sharding committee at predefined intervals. Further, a first message packet from each peer node is received by the reference committee based on which a second message packet is generated enabling each peer node of the block chain network to join one of the sharding committee.

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.