Information Theoretic Analysis of Privacy in a Multiple Query-Response Based Differentially Private Framework

Data privacy or safeguarding data from potential threats has become a critical issue in our data-centric world. Among the developed mechanisms catering to the objective of privacy preservation, differential privacy has emerged as a popular and effective technique which provides the required level of user privacy. In our work, we have information theoretically analyzed differential privacy in a multiple query-response based environment. We have evaluated our model on a real-world database and subsequently evaluated the effects of externally added noise on the resulting privacy. The simulated results confirm the notion that the privacy risk is inversely proportional to the amount of noise added in the system (defined by \( \varepsilon \)).