C. Dwork, Differential Privacy, Proceedings of the 33rd International Colloquium on Automata, Languages and Programming (invited), 2006 Abstract In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius’ goal along the lines of semantic security cannot be achieved. Contrary to intuition, a variant of the result threatens the privacy even of someone not in the database. This state of affairs suggests a new measure, dif- ferential privacy, which, intuitively, captures the increased risk to one’s privacy incurred by participating in a database. The techniques devel- oped in a sequence of papers [8, 13, 3], culminating in those described in [12], can achieve any desired level of privacy under this measure. In many cases, extremely accurate information about the database can be provided while simultaneously ensuring very high levels of privacy. This article was published on 2024-11-22
