8 Privacy-preserving data management and operations

In the previous chapter, we discussed different privacy-enhancing techniques that can be utilized in data mining operations and how to implement the k-anonymity privacy model in practice. In this chapter, we are going to explore a set of other privacy models that the research community has proposed in terms of mitigating the flaws in the 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 of designing a tailormade privacy enriched database management system.

We are now at the eighth chapter of this book. This is the second part of the privacy-preserving data mining and management techniques. Let’s continue our discussion on other privacy models and how to implement them in practice. Towards the end of this chapter, we will be looking at design considerations for a privacy-preserving database system to protect data privacy while processing and managing data for data mining applications.

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

8.2 Privacy Protection beyond k-anonymity

8.2.1 l-diversity

DOES IT LEAK ANY SORT OF INFORMATION?

8.2.2 t-closeness

TRY IT YOURSELF

8.3 Protecting Privacy by Modifying the Data Mining Output

8.4 Privacy Protection in Data Management Systems

8.4.1 Database Security and Privacy: Threats and Vulnerabilities

DATA PROTECTION SCHEMES ARE EMPLOYED BY THE INDUSTRY RIGHT NOW

HOWEVER, IN PRACTICE, PRIVACY ASSURANCE IS VERY CHALLENGING!

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

8.4.3 Attacks on Database Systems

ATTACKS TARGETING DATA CONFIDENTIALITY

ATTACKS TARGETING DATA PRIVACY

8.4.4 Privacy Preserving Techniques in Statistical Database Systems

8.4.5 Toward Designing Tailor-made Privacy Preserving Database System

8.5 Summary

REFERENCES