chapter nine

9 Privacy-preserving data mining and management techniques (Part-2)

 

This chapter covers

  • Widely used privacy models and their characteristics in data mining operations when processing and publishing data
  • Privacy threats and vulnerabilities in database systems
  • Use of privacy protection in today’s database management systems
  • Applying database design considerations and artifacts toward implementing privacy-preserving database system in practice

In the previous chapter, we discussed different privacy enhancing techniques that can be utilized in data mining operations and how to implement k-anonymity privacy model in practice. In this chapter, we are going to explore a set of other privacy models that have been proposed by the research community in terms of mitigating the flaws in k-anonymity model. Towards the end of this chapter, we will be discussing the evolution of data management techniques over the recent past and how these privacy mechanisms can be instrumented in database systems more toward the practical deployment designing a tailormade privacy enriched database management system.

9.1 A Quick Recap on Privacy Protection in Data Processing and Mining

9.2 Privacy Protection beyond k-anonymity

9.2.1 l-diversity

9.2.2 t-closeness

9.3 Protecting Privacy by Modifying the Data Mining Output

9.4 Privacy Protection in Data Management Systems

9.4.1 Database Security and Privacy: Threats and Vulnerabilities

9.4.2 How Probable is a Modern Database System to Leak Private Information?

9.4.3 Attacks on Database Systems

9.4.4 Privacy Preserving Techniques in Statistical Database Systems

9.4.5 Toward Designing Tailor-made Privacy Preserving Database System

9.5 Summary