Differential Privacy Noise Allocation in Independent Media Impression Audits
Privacy noise allocation in media audits requires shifting epsilon from micro-slices to campaign aggregates to keep financial billing errors below two percent.
Laplace Mechanism
Meaning
Mechanism for protecting individual records by adding noise from a laplace distribution to the output of a numerical query. Organizations use the laplace mechanism to achieve pure differential privacy, which provides a strict guarantee that the privacy loss will not exceed a certain level. It is one of the simplest and most widely used techniques for protecting individual privacy in statistical databases.
The amount of noise added depends on the sensitivity of the function being calculated.
Mathematical Basis
The laplace mechanism is built on the property that adding noise with a specific probability density function can hide the presence or absence of any single individual in the data set. This makes the output of the query almost the same regardless of whether a particular person’s data is included. It is highly effective for simple count and sum queries.
Implementation Cost
Using the laplace mechanism involves a tradeoff between the level of privacy and the accuracy of the data. Higher levels of privacy require more noise, which can make the data less useful for certain types of analysis. Companies must decide on the appropriate epsilon value based on their risk tolerance and the needs of their stakeholders.
Noise Application
Adding noise involves a tradeoff between the level of privacy and the accuracy of the data.