1. A computer implemented method for determining a privacy-utility trade off, the method comprising: quantifying privacy content of a private data associated with a user based on uniqueness of information in the private data, wherein the quantifying comprises determining a theoretic randomness and uncertainty of information in the private data of the user, and wherein the private content comprises sensitive information about the user; determining a privacy-utility trade off point model based on analytical analysis of the privacy content of a private data associated with a user, a privacy requirement of the user, and a utility requirement of third party utilizing the private data of the user, wherein the privacy-utility trade off point model is indicative of optimal private data sharing technique with the third party for determining optimal sharing point, and wherein the optimal sharing point is a difference between the privacy content of the private data and that of a transformed private data; identifying privacy settings for the user based on risk appetite of the user, utilizing the determined privacy-utility tradeoff point model, wherein the risk appetite of the user is indicative of the user's utility requirement and private data sharing capabilities; and sanitizing the private data of the user by perturbation based on the identified privacy settings for the user wherein the perturbation reduces a granularity of the private data, and wherein the sanitization further comprises: ascertaining the privacy requirement of the user, and the utility requirement of third party based on the privacy setting of the user; and determining the optimal sharing point based on the privacy-utility trade off point model and the ascertained privacy requirement of the user, wherein the quantifying further comprises computing Kullback-Leibler divergence to determine relative entropy between the private data of the user and a standard non-private data, wherein the divergence is indicative of the privacy content of the private data.