Tutorial — Pinning Down
"Privacy" in Statistical Databases
an agency holding a large database of sensitive personal information --
medical records, census survey answers, web search records, or genetic data,
for example. The agency would like to discover and publicly release global
characteristics of the data (say, to inform policy and business decisions)
while protecting the privacyof individuals'
records. This problem is known variously as "statistical disclosure
control", "privacy-preserving data mining" or "private
This tutorial will describe "differential privacy", a notion which emerged from a recent line of work that seeks to formulate and satisfy rigorous definitions of privacy for such statistical databases. We will begin by discussing "cryptanalysis" for data privacy (deanonymization attacks). Motivated by this, we will discuss differential privacy and related definitions, and then explore techniques for designing differentially private algorithms.
Smith is an associate professor in the Department of Computer Science and Engineering