Overview of Privacy-Preserving Data Analysis and an Introduction to Differential Privacy
In this chapter, we will explore the concept of privacy in the context of big data, along with the associated risks. We will delve into privacy in data analysis, focusing on the trade-off between privacy and utility. Furthermore, we will investigate various privacy-preserving techniques, such as anonymization, k-anonymity, t-closeness, and ℓ-diversity, while also discussing their limitations. Later on, we will introduce one of the key privacy-enhancing approaches, known as differential privacy. We will provide a high-level overview of differential privacy, covering essential concepts such as privacy loss, privacy budgets, and differential privacy mechanisms.
The main topics covered in this chapter include the following:
- Privacy in data analysis:
- Privacy in data analysis, the need for privacy in data analysis, and the objectives of privacy in data analysis
- Privacy-preserving...
