@inproceedings{dwork2006differential,
author = {Dwork, Cynthia},
title = {Differential Privacy},
series = {Lecture Notes in Computer Science},
booktitle = {33rd International Colloquium on Automata, Languages and Programming, part II (ICALP 2006)},
year = {2006},
month = {July},
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, differential privacy, which, intuitively, captures the increased risk to one’s privacy incurred by participating in a database. The techniques developed 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.},
publisher = {Springer Verlag},
url = {http://approjects.co.za/?big=en-us/research/publication/differential-privacy/},
pages = {1-12},
volume = {4052},
isbn = {3-540-35907-9},
edition = {33rd International Colloquium on Automata, Languages and Programming, part II (ICALP 2006)},
}