On the Impact of Multi-dimensional Local Differential Privacy on Fairness

Data collected about individuals is regularly used to make decisions that impact those same individuals. For example, census statistics have important implications for all aspects of daily life, including the allocation of political power, the distribution of federal funds, and research in economics and social sciences. In banking industries, machine learning (ML) models leverage data to proactively monitor customer behavior, reduce the likelihood of false positives, and prevent fraud. In these settings, there is a tension between the need for accurate systems, in which individuals receive what they deserve, and the need to protect individuals from improper disclosure of their sensitive information. Differential privacy (DP) [23] is now widely recognized as the gold standard for providing formal guarantees on the privacy level achieved by an algorithm. However, central DP can only be used on the assumption of a trustworthy server. Local DP (LDP) [32] is a variant that achieves privacy guarantees for each user locally with no assumptions on third-party servers. In other words, LDP ensures that each user's data is locally obfuscated first on the client-side and then sent to the server-side, thus protecting data from privacy leaks on both the client-side and the server-side. Many Big tech companies have deployed LDP-based algorithms to use in their industrial products (e.g., Google Chrome [24] and Apple iOS [4]).