Tata Consultancy Services Limited

Nikhil Girish Patwardhan

Ashim Roy

Rupali Kedar Kulkarni

Nandita Babu

Sachin Premsukh Lodha

Anantha Desik Puranam Hosudurg

Sumit Johri

Prachi Shastri

Pranit Reke

Sumiran Naman

Moksha Suryakant Jivane

Srinivasa Rao Chalamala

Vijayanand Mahadeo Banahatti

Arun Kumar Jindal

Ajeet Kumar Singh

Ashish Diwan

Jayant Sudhakarrao Dani

Sushant S Vale

Mayur Jain

Amol Rajendra Limaye

Aniket Dilip Kulkarni

Ankit Jagtap

Spondita Paul

Anantha Desik

Adesh Ramdas Takawane

Kalaiselvan Kandasamy

Venkata Bala Tripura Sundari Nallamreddy

Eeti Sancheti

Varsha Jagtap

Jevanthi Priva C K

Sarang Kamlesh Barpatre

Sitarama Narayana Bulusu

Moksha Sunil Padsalgi

Shraddha Kapse

Jayant Dani

As discussed earlier, labelling techniques that are available for labelling of unlabelled tabular data use some semi supervised models for identification purposes. However, they require sample labeled data for training purposes. Further, the same labelling model/technique cannot be used for all data types. Present disclosure provides method and system for identifying labels of unlabeled column data. The system uses a hybrid approach i.e., it uses language models, regular expressions and known dictionaries for labelling of unlabelled tabular data. For performing labelling, system first classifies received unlabelled tabular data into one or more data buckets. The system then uses appropriate techniques, based on data types, for identification of labels of unlabeled data present in data buckets. Thereafter, system uses feedback mechanism which will impart maturity to system over time. Finally, once system is matured, system can identify labels for all types of data.

Method and system for identifying labels of unlabelled column data

Existing systems provide data selection for one differential technique considering an analytical problem or synthetic data but not an arrangement for selection of one or more techniques together. The embodiments herein provide a method and system for differential privacy enabled service with hybrid rule management and similarity metrics to select data. The system generates a query table called universal data from the associates tables and databases. The system further based on query on the universal table of single columns or selected columns with different parameters using different privacy rules generates differential private data stored in temp tables/views. The system retrieves queried data of different techniques and parameters interactively viewing the privacy and similarity of each data types of retrieved data with universal data using different metrics like bar charts, Histograms, average, std. and correlation to select right different privacy data of the universal data based on Privacy and similarity tolerances.

System and method for enabling differential privacy techniques

Unique key fields in structured data is a critical characteristic of data which plays significant role in data management. Profiling high volume of data for discovering all possible unique keys with high accuracy is a costly and time taking affair. A method and system for finding one or more unique entities in a data have been provided. The one or more unique entities obtained by this approach are complete and the response time is quick. The method is scalable to increasing volume of data and number of fields. The system is configured to perform the analysis process in multiple phases taking the less volume initially and increasing it gradually, thereby reducing the load on later phases as the unique results found with light volume phases. The method also comprises a time check mechanism after different stages if a user wants to do discovery for limited time.

Method and system for finding one or more unique entities in data

The disclosure generally relates to systems and methods for detecting personally identifiable information (PII). The present systems and methods solve the problem of detecting the PII and the PII column names in the customer database with enhanced accuracy, by developing a PII classification model trained with an enhanced and effective training dataset. An enhanced sub-metadata from the metadata having the plurality of the column names is obtained by using highest match distance values, the string comparator values, and the is PII indicator values. The enhanced sub-metadata comprising the column names that can be easily differentiated as PII columns or non-PII columns. Hence the training dataset and the testing dataset obtained from the enhanced sub-metadata improves the accuracy of the PII classification model. Preventive measures can be taken to protect such detected PII present under the PII columns by employing various data privacy and protection techniques.

Systems and methods for detecting personally identifiable information

This disclosure relates generally to a system and method for masking text within images. Conventionally, image masking approaches have enabled masking but masking PII data which contains sensitive data is still a challenge. The present disclosure includes training and masking phase, wherein during training phase the PII label and values of the input image are captured and stored as co-ordinates in the database. During masking, the test image and the words comprised in the test image are optimized using an OCR technique. The label and value of each pairs are compared with the words comprised in the optimized test image. The comparison results in one or more matching labels and then a masking area is calculated for each matching label. The masking string is generated for each matching label based on the calculated masking area and the original text is masked with the generated string.

System and method for masking text within images

System and method for cheque image data masking are disclosed. In an example, a cheque image and a data file are received, the data file includes data records with values corresponding to sensitive data fields in the cheque image. Further, a template cheque image matching to the cheque image and redacted information associated with the template cheque image are obtained. Furthermore, a blank image snippet is generated for each sensitive data field in the cheque image based on a part of the obtained information about sensitive fields. Moreover, values corresponding to each sensitive field from the input data file are written to the blank image snippet based on the remaining information about sensitive fields. Also, the template cheque image is updated with the image snippet. Data of non-sensitive fields in the cheque image is then copied to the template cheque image, thereby facilitating cheque image data masking.

System and method for cheque image data masking using data file and template cheque image

System and methods for creating one or more variants of test data during various test stages are disclosed. The system and methods facilitate a user to create a primary variant for test data and to store the primary variant of the test data in a test data variant repository. Any changes performed in the test database by the user are identified by using a tracking mechanism to store one or more new variants along with a variant number with respect to the changes performed in the test database in response to the user's request. The primary variant and the new variants are accessed by the user to perform a comparative analysis and to modify the test data at any test stage.

System and method for creating variants in a test database during various test stages

Systems and methods for consistent data generation across an enterprise involves explosion of seed data received in at least one of two or more databases present across the enterprise based on an identified reference data field in the received seed data and a pre-defined pattern. A lightweight map of key-value pairs is created wherein the key is data in the reference data field of at least one data record and the value is a pointer to a transformed value of either the data in the reference data field or at least one data field other than the reference data field, the transformed value being based on the pre-defined pattern. Based on the exploded seed data, the map is continually appended for further data explosion such that consistent data is generated across the enterprise.

Systems and methods for enterprise-wide consistent data generation

Methods and systems for masking data columns in a database are described herein. The method describes obtaining a masking template. Further the method describes associating the masking template with at least one rule and selecting columns based on the at least one rule. The method further describes identifying at least one column from the selected columns for applying masking setup and initiating masking setup on the at least one column based on the masking template.

Data masking setup

The present subject matter relates a testing system for an application. The system includes a test data generation module to generate test data for a program code. The test data generation module in turn includes a relational expression creation module that determines a relational expression corresponding to a set of parameters of the program code based on a rule indicating a format of a valid test data for the parameters. A boundary recognition module identifies a set of boundary values of the parameters based on the relational expression. Further, a solver module then generates valid test data and invalid test data for the parameters based on the boundary values.

Testing system

The present subject matter relates to systems and methods for database sampling. The method comprises identifying at least one query table and one or more associated tables amongst a plurality of tables in a production database, based on filtering criteria. Further, the method comprises generating a key value list for the at least one query table and each of the one or more associated tables based on an order indicated by an order list. Based on the generated key value list, the sample data is extracted in a reverse order indicated by the order list, from the at least one query table and each of the one or more associated tables.

Database sampling

Systems and methods for test data generation are described. In one implementation, the method includes receiving seed data having one or more characteristics. Further, the method includes obtaining a selection criterion indicating a selected portion of the seed data to be transformed. Based on the selection criterion, the seed data is transformed for at least a plurality of iterations to generate test data. The test data comprise a plurality of data sets including a primary data set generated in a first iteration and a secondary data set generated in each subsequent iteration. The primary data set includes transformed data corresponding to the selected portion of the seed data and non-transformed data corresponding to a remaining portion of the seed data and each secondary data set includes transformed data corresponding to the selected portion of the seed data.

Test data generation

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Maharashtra, India

Nikhil Girish Patwardhan