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FSTTCS
2009
Springer

Functionally Private Approximations of Negligibly-Biased Estimators

13 years 10 months ago
Functionally Private Approximations of Negligibly-Biased Estimators
ABSTRACT. We study functionally private approximations. An approximation function g is functionally private with respect to f if, for any input x, g(x) reveals no more information about x than f (x). Our main result states that a function f admits an efficiently-computable functionally private approximation g if there exists an efficiently-computable and negligibly-biased estimator for f. Contrary to previous generic results, our theorem is more general and has a wider application reach. We provide two distinct applications of the above result to demonstrate its flexibility. In the data stream model, we provide a functionally private approximation to the Lp-norm estimation problem, a quintessential application in streaming, using only polylogarithmic space in the input size. The privacy guarantees rely on the use of pseudo-random functions (PRF) (a stronger cryptographic notion than pseudo-random generators) of which can be based on common cryptographic assumptions. The application ...
André Madeira, S. Muthukrishnan
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where FSTTCS
Authors André Madeira, S. Muthukrishnan
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