Abstract: 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.

Abstract: 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.

Abstract: Aspects of optimized software development are described herein. In an implementation, according to a method for optimized software development, specifications of a repetitive-use software asset are published on an open platform over a network to acquire a demand parameter associated with the repetitive-use software asset. The demand parameter is acquired based on the specifications of the repetitive-use software asset. Based on the demand parameter associated with the repetitive-use software asset, a use-commitment indicator is obtained. The use-commitment indicator indicates a commitment for using the repetitive-use software asset. Further, a recommendation for developing the software asset is generated based on the use-commitment indicator.