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Data stream methods look at each new item of the stream, perform a small number of operations while keeping a small amount of memory, and still perform much-needed analyses. However, in many situations, the update speed per item is extremely critical and not every item can be extensively examined. In practice, this has been addressed by only examining every… (More)

We consider the problems of computing the Euclidean norm of the difference of two vectors and, as an application, computing the large components (Heavy Hitters) in the difference. We provide protocols that are approximate but private in the semi-honest model and efficient in terms of time and communication in the vector length N. We provide the following,… (More)

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… (More)

For processing massive data streams, most proposed al-gorithmic methods look at each new item, perform a small number of operations while keeping a small amount of memory, and still perform much-needed analyses. However, in many situations, the update speed per item is very critical and not every item can be extensively examined. In practice, this has been… (More)

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