Optimal error rates for interactive coding I
From MaRDI portal
Publication:5259615
DOI10.1145/2591796.2591872zbMath1315.94122arXiv1312.1764OpenAlexW2040355376MaRDI QIDQ5259615
Madhu Sudan, Mohsen Ghaffari, Bernhard Haeupler
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/1312.1764
Linear codes (general theory) (94B05) Bounds on codes (94B65) Error probability in coding theory (94B70)
Related Items (12)
Interactive communication with unknown noise rate ⋮ Synchronization Strings: Channel Simulations and Interactive Coding for Insertions and Deletions ⋮ Interactive non-malleable codes ⋮ List and Unique Coding for Interactive Communication in the Presence of Adversarial Noise ⋮ Making Asynchronous Distributed Computations Robust to Channel Noise ⋮ Reliable communication over highly connected noisy networks ⋮ Unnamed Item ⋮ Interactive Coding for Interactive Proofs ⋮ Capacity of Interactive Communication over Erasure Channels and Channels with Feedback ⋮ Palette-alternating tree codes ⋮ Fast Interactive Coding against Adversarial Noise ⋮ Unnamed Item
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: Optimal error rates for interactive coding I
