Tata Consultancy Services Limited

Ashim Roy

Nikhil Girish Patwardhan

Rupali Kedar Kulkarni

Sachin Premsukh Lodha

Nandita Babu

Sumit Johri

Vijayanand Mahadeo Banahatti

Anantha Desik Puranam Hosudurg

Mayur Jain

Amol Rajendra Limaye

Sumiran Naman

Rahul Krushna Ghodeswar

Shirish Damle

Ashvini Sakharam Mandpe

Srinivasa Rao Chalamala

Arun Kumar Jindal

Ajeet Kumar Singh

Ashish Diwan

Jayant Sudhakarrao Dani

Moksha Suryakant Jivane

Prachi Shastri

Pranit Reke

Varsha Jagtap

Venkata Bala Tripura Sundari Nallamreddy

Kalaiselvan Kandasamy

Adesh Ramdas Takawane

Anantha Desik

Spondita Paul

Aniket Dilip Kulkarni

Eeti Sancheti

Iyengar Venkatachary Srinivasan

Anushka Sharma

Nakhil Girish Patwardhan

Sitarama Narayana Bulusu

Nisha Ravindra Shetty

Moksha Sunil Padsalgi

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

This disclosure relates to a system and method for at source data masking and discovery of unique identifier for at-source masking. The method reads a table of production database comprising sensitive column from a source database for at source data masking. A unique identifier column is identified, and a temporary table is created which has three or more columns. Columns of temporary table comprises a sensitive column from the table of production database, a column for masked data of sensitive column and a unique identifier column. Sensitive column of the temporary table is masked using a known masking technique and the original data of the sensitive column and the masked data of the sensitive column is inserted into the temporary table. Finally, the production database is updated with the masked data of the sensitive column.

System and method for at-source masking of sensitive 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 systems and methods for masking and unmasking of sensitive data. The present systems and methods solve the problems of consistency of the data masking, by using a random index and a masked index with use of regular expression concept. An additional random key produce different masked data versions, however the original form of the sensitive data is achieved with any masked data version. Plurality of masked data versions are generated by masking the sensitive input dataset, where the plurality of masked data versions comprises same format of the sensitive input dataset. The generated masked data versions are secured and hard to predict the original form of the sensitive input dataset by authorized or unauthorized environments. Also, the present method consume less processing time, as the masking process and the unmasking process make operations on the indexes rather than with the original dataset.

Systems and methods for masking and unmasking of sensitive data

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

A method and system is provided for generating a group of phonetically similar masked data. The present application provides a method and system for generating a group of phonetically similar masked data; comprises preprocessing of input dataset values comprising a list of fictitious data values to be used as masked data; determining a plurality of groups of phonetically similar data values present in the dataset list; and deriving metaphone for each input data value to be masked; generating a first numeric code from derived metaphone value of input data value to be masked; selecting one group of phonetically similar data values out of the plurality of groups of phonetically similar data values based on the generated first numeric code; and generating a second numeric code from input data value for selecting a masked value from a plurality of fictitious data group.

Method and system for generating phonetically similar masked data

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

A computer implement format preservation based masking system and method is provided. The system obtains a first set of letters and a private key, and encrypts the first set of letters to obtain an encrypted letters list using the first set and private key. The encrypted letters list comprises a set of encrypted letters. A dynamic map is generated based on the encrypted letters, which includes one or more keys, each key being specific to a letter in the first set letters. A position of each of maskable letters in a second set of letters is calculated using the dynamic map, and performs masking of the maskable letters based on the position of each of the maskable letters to obtain masked data using the dynamic map.

Format preservation based masking system and method

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

Disclosed is a method and system to provide management of test data, the management performed during at least one stage associated with lifecycle of the test data. The system comprises a processing engine, a categorization module, a privacy regulation module, a meta-data analyzer, and an output generation module. The processing engine configured to generate the test data in response to a test data request. The processing engine further comprises of the categorization module configured to categorize the test data request. The processing engine further comprises of the privacy regulation module configured to model at least one privacy regulation in accordance with a geographical location and an enterprise domain. The processing engine further comprises the meta-data analyzer configured to analyze an imported meta-data. The system further comprises of the output generation module configured to provide the test data so requested.

System and method to provide management of test data at various lifecycle stages

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.

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Pune, India

Ashim Roy