# Exploiting Metric Structure for Efficient Private Query Release

@article{Huang2012ExploitingMS, title={Exploiting Metric Structure for Efficient Private Query Release}, author={Zhiyi Huang and Aaron Roth}, journal={ArXiv}, year={2012}, volume={abs/1211.7302} }

We consider the problem of privately answering queries defined on databases which are collections of points belonging to some metric space. We give simple, computationally efficient algorithms for answering distance queries defined over an arbitrary metric. Distance queries are specified by points in the metric space, and ask for the average distance from the query point to the points contained in the database, according to the specified metric. Our algorithms run efficiently in the database…

## 13 Citations

### Efficient Private Query Release via Polynomial Approximation

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It is shown that there exists a computationally efficient $\varepsilon$-differentially private mechanism that releases a query class parametrized by additively separable Holder continuous functions, and that the accuracy can be significantly boosted.

### Optimal Differentially Private Algorithms for k-Means Clustering

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It is proved a matching lower bound that no (ε, δ)-differentially private algorithm can guarantee Wasserstein distance less than Ømega (Φ2) and, thus, the positive result is optimal up to a constant factor.

### Differentially Private Data Publishing and Analysis: A Survey

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This survey compares the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy.

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An abstract model of differential privacy is presented in which a differential privacy problem is modeled as finding a randomized mapping between two metric spaces and the experiments show that the mechanisms have more accurate results than the state of the art mechanisms.

### Differentially private data publishing: Non-interactive setting

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This chapter present the non-interactive setting in data publishing, including batch queries publishing, contingency table publishing and synthetic dataset publishing, which means all queries are given to the curator at one time.

### Technical Questions About Differential Privacy 2 . 1 Efficient Algorithms for Releasing Conjunctions

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Technical Questions About Differential Privacy 2.1 Efficient Algorithms for Releasing Conjunctions and Reduction Hypothesis Under the -Matrix Mechanism and more.

### The Policies of Designing Differentially Private Mechanisms: Utility First vs. Privacy First

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This paper realizes that designing a differentially private mechanism can be considered as finding a randomized mapping between two metric spaces and finds that the sensitivity-based methods are those just using the metric about utility to construct mechanisms.

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This chapter presents three methods that apply differential privacy to achieve location privacy for LBSs: the geo-indistinguishability method, the synthetic differentially private trajectory Publishing method, and the hierarchical location data publishing method, with an emphasis on the last one.

### An Antifolk Theorem for Large Repeated Games

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It is argued that in large games (n player games in which unilateral deviations by single players have only a small impact on the utility of other players), many monitoring settings naturally lead to signals that satisfy (ε, γ)-differential privacy for ε and γ tending to zero as the number of players n grows large.

### Differentially Private Kernel Support Vector Machines Based on the Exponential and Laplace Hybrid Mechanism

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This paper proposes a new differentially private algorithm for the kernel SVMs based on the exponential and Laplace hybrid mechanism named DPKSVMEL and theoretically proves that the DP KSVMEL algorithm satisfies differential privacy.

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