Abstract: The present disclosure a method for data regulations-aware cloud storage and processing service allocation. Conventional approaches fail to address the technical problem of data placement for storage as well as processing, considering multiple criteria. Further, the conventional approaches fail to address compliance with data regulations, tier pricing policy for multiple Cloud Service Providers (CSPs) which impacts storage and processing center selection and constraint satisfaction. The present disclosure proposes a joint optimization model for the selection of storage and processing services from multiple cloud service providers, taking into practical consideration of data regulations and tiered pricing, which has not been addressed in the prior art. To solve this hard multi-objective combinatorial optimization problem, the present disclosure utilizes a cost-reduction-based algorithm for obtaining optimal solution.

Abstract: This disclosure relates generally to data anonymization using clustering techniques. In Typically, data anonymization using global recoding can overgeneralize the data. However, preservation of information while anonymization the data is of equal importance as obscuring the relevant information that can be used by the attackers. The disclosed method and system utilized attribute taxonomy tree for generalization to optimize the generalization of the records. The disclosed method uses clustering-based approach and after clustering, each cluster is solved independently using ILP model for K-Anonymization. The ILP model is solved by generalizing the value of the attributes. Sometimes, even after clustering the number of possible patterns is large, thus the disclosed method generates patterns on the fly during multiple iterations.

Abstract: This disclosure relates generally to data anonymization and more particular y risk-aware data anonymization. Conventional data anonymization systems either replace PII/sensitive attributes with random values or shuffles them, that causes huge data distortion affecting the data utility. The goal of publishing data is best achieved when privacy is balanced with utility of data, Moreover, to ensure privacy, assessing the risk of disclosure is important. The proposed system provides a pipeline for analysis of data patterns to understand the associated risk level of re-identification of records. Further, based on the identified risks with the records the system anonymizes the data following a pattern based anonymization approach wherein data is clustered and for each cluster distinct patterns are identified such that the information loss is minimal.

Abstract: Existing data residency compliance techniques suffer from inherent drawbacks to discover the spread of data, understanding the data residency regulations and semantics behind them and most importantly placement of data in cloud datacenters such that it is data residency compliant. Embodiments herein provide a method and system for optimizing placement of data to a cloud datacenter complying data residency regulations. The system selects one serving cloud datacenter for a user center. The selection considers three conflicting objectives such as minimum data placement cost, provide good quality of service (i.e. latency) and to comply with data residency regulations. The system essentially covers data residency compliance problem in three phases namely, violation detection, decision support and recommendation.

Abstract: This disclosure relates generally to data anonymization using clustering techniques. In Typically, data anonymization using global recoding can overgeneralize the data. However, preservation of information while anonymization the data is of equal importance as obscuring the relevant information that can be used by the attackers. The disclosed method and system utilized attribute taxonomy tree for generalization to optimize the generalization of the records. The disclosed method uses clustering-based approach and after clustering, each cluster is solved independently using ILP model for K-Anonymization. The ILP model is solved by generalizing the value of the attributes. Sometimes, even after clustering the number of possible patterns is large, thus the disclosed method generates patterns on the fly during multiple iterations.

Abstract: This disclosure relates generally to data anonymization and more particular y risk-aware data anonymization. Conventional data anonymization systems either replace PII/sensitive attributes with random values or shuffles them, that causes huge data distortion affecting the data utility. The goal of publishing data is best achieved when privacy is balanced with utility of data, Moreover, to ensure privacy, assessing the risk of disclosure is important. The proposed system provides a pipeline for analysis of data patterns to understand the associated risk level of re-identification of records. Further, based on the identified risks with the records the system anonymizes the data following a pattern based anonymization approach wherein data is clustered and for each cluster distinct patterns are identified such that the information loss is minimal.

Abstract: This disclosure relates generally to malware detection, and more particularly to system and method for detecting and mitigating ransomware threats. For a User Equipment being monitored, the system performs a behavior analysis of corresponding file system to determine whether any anomalous behavior that would amount to a ransomware threat is associated with flies associated with the file system change, if present, then the system virtualizes the file system on the fly. If information pertaining to the identified anomalous behavior is present in any of the reference databases in the system, then all the I/O calls are terminated or the file system is virtualized for rest of the session. If data pertaining to the identified anomalous behavior is not found in any of the associated databases, then new behavioral features and structural patterns of the identified anomalous behavior and the associated processes are extracted, and the reference databases are updated accordingly.

Abstract: Systems and methods for sensitive audio zone rearrangement are provided that protects confidential and sensitive information such as user identifier during query processing and authentication. The sensitive information rearrangement system generates or permutes the actual user identifier in a privacy preserving manner. The sensitive information is extracted from an input being either a speech or DTMF tones, and a virtual user identifier is generated in real time, that is specific to a transaction to be performed, or a query initiation by a user in real-time. The sensitive information is rearranged which can be either DTMF tone or speech of user to generate the virtual user identifier.

Abstract: The present disclosure provides systems and methods to estimate Importance Measure (IM) and temporal Importance Measure (IM) for any type of data in a distributed enterprise network by involving direct and indirect stakeholders of the data in the estimation process. Firstly data type of the received data is identified. Data type plugins including pre-defined classes, IM matrix and Temporal IM matrix are loaded for the identified data type. Extracted features from the data are appropriately mapped against pre-defined classes and then the IM is estimated. Temporal IM is estimated taking into account the current time and the rate of change.

Abstract: The present disclosure provides systems and methods to optimize data backup in a distributed enterprise system by firstly generating a set of unique files from all the files available in the enterprise. A backup set comprising files to be backed up are then generated from the set of unique files and backup is scheduled in the order in which the files to be backed up are identified. Unique files are generated based on file sharing patterns and communications among users that enable generating a social network graph from which one or more communities can be detected and deduplication can be performed on the files hosted by client systems in these communities thereby conserving resources.

Abstract: This disclosure relates generally to malware detection, and more particularly to system and method for detecting and mitigating ransomware threats. For a User Equipment being monitored, the system performs a behavior analysis of corresponding file system to determine whether any anomalous behavior that would amount to a ransomware threat is associated with flies associated with the file system change, if present, then the system virtualizes the file system on the fly. If information pertaining to the identified anomalous behavior is present in any of the reference databases in the system, then all the I/O calls are terminated or the file system is virtualized for rest of the session. If data pertaining to the identified anomalous behavior is not found in any of the associated databases, then new behavioral features and structural patterns of the identified anomalous behavior and the associated processes are extracted, and the reference databases are updated accordingly.

Abstract: Systems and methods for sensitive audio zone rearrangement are provided that protects confidential and sensitive information such as user identifier during query processing and authentication. The sensitive information rearrangement system generates or permutes the actual user identifier in a privacy preserving manner. The sensitive information is extracted from an input being either a speech or DTMF tones, and a virtual user identifier is generated in real time, that is specific to a transaction to be performed, or a query initiation by a user in real-time. The sensitive information is rearranged which can be either DTMF tone or speech of user to generate the virtual user identifier.

Abstract: The present disclosure provides systems and methods to estimate Importance Measure (IM) and temporal Importance Measure (IM) for any type of data in a distributed enterprise network by involving direct and indirect stakeholders of the data in the estimation process. Firstly data type of the received data is identified. Data type plugins including pre-defined classes, IM matrix and Temporal IM matrix are loaded for the identified data type. Extracted features from the data are appropriately mapped against pre-defined classes and then the IM is estimated. Temporal IM is estimated taking into account the current time and the rate of change.

Abstract: The present disclosure provides systems and methods to optimize data backup in a distributed enterprise system by firstly generating a set of unique files from all the files available in the enterprise. A backup set comprising files to be backed up are then generated from the set of unique files and backup is scheduled in the order in which the files to be backed up are identified. Unique files are generated based on file sharing patterns and communications among users that enable generating a social network graph from which one or more communities can be detected and deduplication can be performed on the files hosted by client systems in these communities thereby conserving resources.