  The avalanche of AI deployment and its security-privacy concerns are two
sides of the same coin. Article 17 of GDPR calls for the Right to Erasure; data
has to be obliterated from a system to prevent its compromise. Extant research
in this aspect focuses on effacing sensitive data attributes. However, several
passive modes of data compromise are yet to be recognized and redressed. The
embedding of footprints of training data in a prediction model is one such
facet; the difference in performance quality in test and training data causes
passive identification of data that have trained the model. This research
focuses on addressing the vulnerability arising from the data footprints. The
three main aspects are -- i] exploring the vulnerabilities of different
classifiers (to segregate the vulnerable and the non-vulnerable ones), ii]
reducing the vulnerability of vulnerable classifiers (through data obfuscation)
to preserve model and data privacy, and iii] exploring the privacy-performance
tradeoff to study the usability of the data obfuscation techniques. An
empirical study is conducted on three datasets and eight classifiers to explore
the above objectives. The results of the initial research identify the
vulnerability in classifiers and segregate the vulnerable and non-vulnerable
classifiers. The additional experiments on data obfuscation techniques reveal
their utility to render data and model privacy and also their capability to
chalk out a privacy-performance tradeoff in most scenarios. The results can aid
the practitioners with their choice of classifiers in different scenarios and
contexts.
