Privacy-Preserving Data Publishing (PPDP) has become a critical issue for companies and organizations that would release their data. Many organizations collect and distribute personal data for a variety of different purposes, including demographic and public health research. In these situations, the data distributor is often faced with a dilemma: how to publish this personal data for analysis purposes without endangering the privacy of the concerned individuals? Disseminating such information without the privacy scare is an important problem. On one hand, the data publishers need to protect the privacy of individuals and on the other hand, it is also extremely important to preserve the usefulness of the data for the researchers. In this dissertation, we mainly focus on crafting the notions of privacy in various settings. We show that spatial indexes are extremely efficient for data publication tasks due to their ability to scale up. Also, sequential data is being increasingly employed in a wide variety of applications and the publication of sequential data is of utmost importance for the betterment of these applications.