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Differential Privacy - PowerPoint PPT Presentation

Differential Privacy. REU Project Mentors: Darakhshan Mir James Abello Marco A. Perez. In an ideal world…. We would like to be able to study data as freely as possible. What is Differential Privacy?.

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Differential Privacy

REU Project Mentors:Darakhshan Mir James Abello

Marco A. Perez

• We would like to be able to study data as freely as possible

• One’s participation in a statistical database should not disclose any more information that would be disclosed otherwise.

• Neighboring databases can only differ by, at most, one entry.

x

x'

ε-Differential Privacy

Global Sensitivity

• GSof f, is the maximum change in f over all neighboring instances

GSf≤ |f(x)-f(x')|

• Assume f is the query How many people are 23 years old, can you compute the global sensitivity?

x

x'

Laplace Distribution and its properties

• The same definition of privacy can be applied to graphs.

• Node-differential Privacytwo graphs are neighbors if they differ by at most one node and all of its incident edges.

• Edge-differential PrivacyTwo graphs are neighbors if they differ by at most one edge

• The maximum amount, over the domain of the function, that any single argument to f can change the output.

Local Sensitivity

Smooth Sensitivity

• Stochastic Kronecker Graphs

• Exponential Random Graph Model

• Theoretically describe the growth of smooth sensitivity in the mentioned random graph models.

• Study graph transformations from a Differentially Private perspective and their implementation