Hands-On Differential Privacy

Chapter 12. Defining Privacy Loss Parameters of a Data Release

Featuring Jayshree Sarathy¹

Differential privacy delineates a trade-off between privacy and utility, with both attributes influenced by various parameters. Taking advantage of this trade-off requires understanding how to set these parameters to achieve your data curation and analysis goals. These choices must be informed by contextual needs around privacy and accuracy—as a choice of parameters may be appropriate in one situation and ineffective in another—and potentially with input from various parties who have a stake in the data.

Some of the decisions that are described in this chapter will be made by the data curator, an individual or organization that is responsible for making decisions around access and disclosure limitations for the data set in question. Other decisions must be made by a data analyst, who must work within the constraints set by the data curator to make the best possible uses of the data for their analysis goals.

Understanding the various parameters that affect privacy and utility is central to making good decisions as a DP data curator and for creating useful analyses within these constraints as a data analyst. Furthermore, it is important to be able to communicate these decisions to other stakeholders to allow them to work appropriately with the data. Fortunately, numerous technical methods facilitate choosing these parameters. Like all matters in differential privacy, there are benefits and ...