Moni Naor

Privacy of Dynamic Data: Continual Observation and Pan Privacy

Based on work by Cynthia Dwork, Moni Naor, Toni Pitassi, Guy Rothblum and Sergey Yekhanin. 

Research in the area of privacy of data analysis has been flourishing
recently, with a rigorous notion such as differential privacy regarding
the desired level of privacy and sanitizing algorithms matching the
definition for many problems. Most of the work in the area assumes that
the data to be sanitized is fixed. However, many applications of data
analysis involve computations of changing data, either because the entire
goal is one of monitoring, e.g., of traffic conditions, search trends, or
incidence of influenza, or because the goal is some kind of adaptive
optimization, e.g., placement of data to minimize access costs.

In this talk I will describe work on providing guarantees for dynamically
changing data. Issues that arise include:
* How to provide privacy even when the algorithm has to constantly output
the current value of some function of the data (Continual Observation)
* How to assure privacy even when the internal state of the sanitizer may
be leaked (Pan Privacy). Here we aim to design algorithms that never store
sensitive information about individuals, so in particular collectors of
confidential data cannot be pressured to permit data to be used for
purposes other than that for which they were collected.

One problem we will concentrate on is that of providing a a public
`counter' that counts the number of times a resource has been accessed,
but does not leak any information about the presence or absence of
individual increments.

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