= Cynthia Dwork
{c}
<Cynthia Dwork> develops theoretical foundations for privacy-preserving data analysis. With <Aaron Roth>, she coauthored <The Algorithmic Foundations of Differential Privacy by Cynthia Dwork and Aaron Roth>. For neighboring datasets $D,D'$ differing in one individual's contribution, pure $\epsilon$-<differential privacy> requires, for every output event $S$,
$$
\Pr[M(D)\in S]\le e^\epsilon\Pr[M(D')\in S].
$$
The quantifier over events protects against more than a few preselected attacks.<The Algorithmic Foundations of Differential Privacy by Cynthia Dwork and Aaron Roth>[§2.3, Definition 2.4 with delta = 0]{ref}
For example, releasing an exact count can disclose participation when it changes from zero to one. A randomized mechanism trades some accuracy for a bound on how distinguishable those two situations become. Dwork's research account places this work within the use of <theoretical computer science> to formalize societal problems.https://dwork.seas.harvard.edu/[Research description]{ref}
\Image[https://dwork.seas.harvard.edu/sites/g/files/omnuum6186/files/styles/hwp_1_1__360x360_scale/public/dwork/files/cynthia_headshot_1.20.jpg?itok=GICVT3f6]
{title=Portrait of <Cynthia Dwork>}
{source=https://dwork.seas.harvard.edu/}
{width=240}
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