Tata Consultancy Services Limited

Mangesh Sharad Gharote

Sachin Premsukh Lodha

Arun Ramamurthy

Sutapa Mondal

Saket Kumar Saurabh

Vijayanand Mahadeo Banahatti

Shree Nivas

Shubhro Shovan Roy

Pankaj Kumar Sahu

Rishabh Kumar

Girish Keshav Palshikar

Kumar Mansukhlal Vidhani

Rajiv Radheyshyam Srivastava

Rohit Gupta

Abhay Sodani

Nitin Ganesh Phuke

Ankita Jain

Gokul Chettoor Jayakrishnan

Gurpriya Kaur Bhatia

This disclosure relates generally to method and system for providing data privacy in service operations. Identifying risk arising from processing of user request in service operations is time consuming and monitoring insider threats is a difficult task. The method includes receiving a user request for a service comprising a plurality of sensitive attributes and further a vulnerability rating is assigned to each sensitive attribute. To mitigate the risk, the method estimates a disclosure proportion for the plurality of sensitive attributes and a masking operation is performed. Further, a sensitivity score is computed to allocate the user request to an agent associated with an agent registry with enabled data privacy and minimal data exposure. The agent is monitored by computing an agent mis-usability score to assess an insider threat risk. Additionally, a feedback alert is notified to autotune the plurality of privacy settings.

Method and system for providing data privacy in service operations

The embodiments of present disclosure herein address the need of minimizing web services relocation and user centers reallocation to comply with data residency regulations and change in latency threshold for web services based on user demands. The method and system provide a framework that assists enterprises in migrating web services and user center allocation to different data centers with lower additional operational cost from its current configurations and minimal changes (migrations). In case of change in data regulations and invocation frequency from users demand, non-compliant users are allocated to compliant data centers with minimal changes in the original configuration. Though the key decision is to serve the customers effectively, web services need to be deployed across a finite number of servers, there are multiple sub-problems such as minimizing latency and reduction in operational cost that needs to be addressed.

System and method for web services relocation and users reallocation for data residency compliance

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.

Method and system for data regulations-aware cloud storage and processing service allocation

Finding optimal solutions to Web Service Location Allocation Problem (WSLAP) using exhaustive algorithms and exact approaches is not practical. Computational time required to solve WSLAP using exact approaches increases exponentially with the problem size. The disclosure herein generally relates to service deployment, and, more particularly, to a method and system for web service location allocation and virtual machine deployment. The system identifies a plurality of web-services that are associated with the WSLAP and then decomposes the WSLAP to a plurality of sub-problems. For each sub-problem, the system determines at least one non-dominating solution, which are then merged to generate the solution for the WSLAP. The generated solution to the WSLAP can be used for perform Virtual Machine (VM) deployment under uncertainties, using a stochastic approach, wherein the uncertainties refer to dynamic change in requirements in terms of parameters such as but not limited to configuration, and cost.

Method and system for web service location allocation and virtual machine deployment

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. Herein, the system trades-offs latency with data placement cost. Further, the system considers user center location, determined violation of data residency regulations, cost of movement from one datacenter to another, and a predefined threshold latency.

System and method for data residency compliant data placement in cloud

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.

System and method for data anonymization using optimization techniques

The disclosure relates to sequencing of asset segments of privacy policies. The asset segments are sequenced based on a set of constraints. In an embodiment the asset segments are sequenced using a set of pre-defined predecessors and a set of pre-defined successors of each asset segment through a feasible sequence generation technique and a sequence generation technique based on the constraints, wherein the constraints are preferences associated with the source entity and the target entity. Hence the disclosure bridges a communication gap between the source entity and the target entity by optimally displaying the most relevant privacy policy (mapped to the asset segments) based on the constraints associated with the source entity and the target entity. Further the disclosed system also determines a violation factor that represents a conflict between the preferences associated with the source entity and the target entity.

Method and system for sequencing asset segments of privacy policy

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.

System and method for risk aware data anonymization

Protecting consumer data is a key responsibility of an organization. The method of protection need to compare the data with policy documents. Ontology and a threshold to select an optimal match plays a key role in such comparison. The conventional automatic threshold computation methods are complex and not based on semantic similarity. The present disclosure generates an Entity Relationship (ER) model from an input document and is converted into a first ontology. The first ontology and a second ontology obtained from a relational database are compared by an ontology matching algorithm. Further, the plurality of many to many correspondences are optimized to one to one correspondence by an optimization method. Further, a plurality of optimal one to one correspondence is generated based on a threshold. The threshold is computed based on symmetric and transitive property. Further, semantically similar entities are selected based on the optimal one to one correspondence.

System and method for identifying semantic similarity

The disclosure relates to a method and a system for sequencing asset segments of privacy policy using optimization techniques. The asset segments are sequenced based on several constraints associated with preferences of a source entity and a target entity. Further the disclosure also determines a target entity violation factor that represents a conflict between the preferences associated with the source entity and the target entity. In an embodiment the disclosed method and system optimally sequences the plurality of asset segments using several optimization techniques that include a basic sequence generation technique, a source entity sequence optimizing technique and a target entity sequence optimizing technique considering constraints. Hence the disclosure bridges a communication gap between several stakeholders by optimally displaying the most relevant privacy policy (mapped to the asset segments) considering the constraints or preferences associated with both the source entity and the target entity.

Method and system for sequencing asset segments of privacy policy using optimization techniques

This disclosure relates generally to selection of cloud service providers in a multi-cloud. The selection of cloud service providers (CSPs) in a multi-cloud environment is challenging as it depends on multiple criteria such as security, usability, latency, and service cost. In an embodiment, for each CSP and multi-CSP combination, two decision problems are solved; namely, a VM selection problem and a cloud region selection problem, taking latency requirement and DR laws constraints into consideration. By solving the VM selection and CC selection problems, the service cost and the user to cloud region mapping are estimated. Using said estimates and other qualitative criteria, a multi-cloud multi-criteria decision problem is solved using MCDM techniques to rank the CSP combinations for each application. Based on the ranks and a constraint on a service cost of the entity one of a CSP and a CSP combination is selected for each application.

System and method for selection of cloud service providers in a multi-cloud

Data is an asset to any organization and any breach to the data during task allocation to agents may lead to serious damage to organizations including loss of consumer confidence, trust, reputation, financial penalties and the like. Conventional methods mainly focus on the allocating task to agents based on user satisfaction, overall throughput and maximize revenue and less focus is given to data privacy. The present subject matter overcomes the limitations of the conventional methods for task allocation by utilizing a dynamic data exposure analysis method, which enables seamless upgrading of the data access policy and or control. Here, a data exposure is monitored based on a data exposure score, dynamic identification of conflicting tasks and a dynamic privacy budget. The data exposure score is calculated in two execution points. Finally all the values are updated in the system for utilization in the further privacy enabled task allocation.

Method and system for privacy enabled task allocation

This disclosure relates generally to automatic conversion of text-based privacy policy (PP) to video format. Typically, the PP are cognitively loaded with the content and hence difficult to read and understand. The disclosed method and system facilitate in automatic conversion of text-based PP to video format. Said video formats may incorporated in animated forms for ease of understanding of the user. The disclosed system identifies segments from target PP and maps them to template PP segments stored in a template repository. The template PP segments selected from the template repository are mapped to short video templates or snippets stored in a video repository to identify video templates representative of the selected template segments. The selected video templates are sequenced in an optimal manner considering preferences and dependencies to obtain the portions of or complete target PP.

Automated conversion of text based privacy policy to video

To evaluate performance of organizational units using Human Capital Value (HCV), the input data spread across an enterprise may be received from a user. The input data includes employee data, project related data, and organizational unit data for performance evaluation. The input data is analyzed for generating HCV variables. The HCV variables are stored in a repository (). Further, the HCV variables may be parsed to determine an optimal set of variables. Based on the parsing, an efficiency of each organizational unit is computed. The computing is based on a Multi Criteria Decision Analysis (MCDA) technique. Based on the computing, the organizational units are ranked in a decreasing order of efficiency.

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Pune, India

Mangesh Sharad Gharote