Cost-benefit analysis is required for making good business decision. This analysis is crucial in the field of privacy-preserving data publishing. In the economic trade of data privacy and utility, organization has the obligation to respect privacy of individuals. They intend to maximize the utility in order to earn revenue and also aim to achieve the acceptable level of privacy. In this thesis, we study the privacy and utility trade-offs and propose an analytical cost model which can help organization in better decision making subject to sharing customer data with another party. We examine the relevant cost factors associated with earning the revenue and the potential damage cost. Our proposed model is suitable for health information custodians (HICs) who share raw patient electronic health records (EHRs) with another health center or health insurer for research and commercial purposes. Health data in its raw form contain significant volume of sensitive data and sharing this data raises issues of privacy breach. Our analytical cost model could be utilized for nonperturbative and perturbative anonymization techniques for relational data. We show that our approach can achieve optimal value as per selection of each privacy model, namely, K-anonymity, LKC-privacy, and ϵ-differential privacy and their anonymization algorithm and level, through extensive experiments on a real-life dataset
