From hierarchical partitions to hierarchical covers
From MaRDI portal
Publication:5259570
DOI10.1145/2591796.2591864zbMath1315.68257arXiv1304.8135OpenAlexW2074149381MaRDI QIDQ5259570
Publication date: 26 June 2015
Published in: Proceedings of the forty-sixth annual ACM symposium on Theory of computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1304.8135
Graph theory (including graph drawing) in computer science (68R10) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05)
Related Items (11)
On Locality-Sensitive Orderings and Their Applications ⋮ Truly Optimal Euclidean Spanners ⋮ Dynamic Tree Shortcut with Constant Degree ⋮ Reliable Spanners for Metric Spaces ⋮ New Doubling Spanners: Better and Simpler ⋮ Vertex Fault-Tolerant Geometric Spanners for Weighted Points ⋮ Sometimes Reliable Spanners of Almost Linear Size. ⋮ On Locality-Sensitive Orderings and Their Applications ⋮ Vertex fault-tolerant spanners for weighted points in polygonal domains ⋮ The Greedy Spanner Is Existentially Optimal ⋮ Local routing in a tree metric \(1\)-spanner
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Efficient algorithms for privately releasing marginals via convex relaxations
- The Geometry of Differential Privacy: The Small Database and Approximate Cases
- Faster Algorithms for Privately Releasing Marginals
- Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
- Privately Releasing Conjunctions and the Statistical Query Barrier
- On the geometry of differential privacy
- Interactive privacy via the median mechanism
- The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
- Lower Bounds in Differential Privacy
- Iterative Constructions and Private Data Release
- Characterizing the sample complexity of private learners
- Faster private release of marginals on small databases
- Bounds on the Sample Complexity for Private Learning and Private Data Release
- Differential Privacy and the Fat-Shattering Dimension of Linear Queries
- Our Data, Ourselves: Privacy Via Distributed Noise Generation
- New Efficient Attacks on Statistical Disclosure Control Mechanisms
- Collusion-secure fingerprinting for digital data
- On the complexity of differentially private data release
- Advances in Cryptology – CRYPTO 2004
- Answering n {2+o(1)} counting queries with differential privacy is hard
- Theory of Cryptography
This page was built for publication: From hierarchical partitions to hierarchical covers