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Calibrating Noise to Sensitivity in Private Data Analysis
TLDR
The study is extended to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f, which is the amount that any single argument to f can change its output. Expand
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data
TLDR
This work proposes two primitives: a fuzzy extractor extracts nearly uniform randomness R from its biometric input; the extraction is error-tolerant in the sense that R will be the same even if the input changes, as long as it remains reasonably close to the original. Expand
Smooth sensitivity and sampling in private data analysis
TLDR
This is the first formal analysis of the effect of instance-based noise in the context of data privacy, and shows how to do this efficiently for several different functions, including the median and the cost of the minimum spanning tree. Expand
Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
TLDR
This work provides new algorithms and matching lower bounds for differentially private convex empirical risk minimization assuming only that each data point's contribution to the loss function is Lipschitz and that the domain of optimization is bounded. Expand
What Can We Learn Privately?
TLDR
This work investigates learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in the contexts where aggregate information is released about a database containing sensitive information about individuals. Expand
Local, Private, Efficient Protocols for Succinct Histograms
TLDR
Efficient protocols and matching accuracy lower bounds for frequency estimation in the local model for differential privacy are given and it is shown that each user need only send 1 bit to the server in a model with public coins. Expand
Analyzing Graphs with Node Differential Privacy
TLDR
A generic, efficient reduction is derived that allows us to apply any differentially private algorithm for bounded-degree graphs to an arbitrary graph, based on analyzing the smooth sensitivity of the 'naive' truncation that simply discards nodes of high degree. Expand
Private Analysis of Graph Structure
TLDR
This work extends the approach of Nissim et al. to a new class of statistics, namely k-star queries, and gives hardness results indicating that the approach used for triangles cannot easily be extended to k-triangles. Expand
Private Convex Empirical Risk Minimization and High-dimensional Regression
We consider differentially private algorithms for convex empirical risk minimization (ERM). Differential privacy (Dwork et al., 2006b) is a recently introduced notion of privacy which guarantees thatExpand
Calibrating Noise to Sensitivity in Private Data Analysis
TLDR
A very clean definition of privacy---now known as differential privacy---and measure of its loss are provided, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the {\em sensitivity} of the function $f$. Expand
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