Order-Revealing Encryption and the Hardness of Private Learning
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Publication:2796125
DOI10.1007/978-3-662-49096-9_8zbMath1388.94037arXiv1505.00388OpenAlexW744425561MaRDI QIDQ2796125
Publication date: 23 March 2016
Published in: Theory of Cryptography (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1505.00388
Related Items (8)
The MMap strikes back: obfuscation and new multilinear maps immune to CLT13 zeroizing attacks ⋮ Separating Computational and Statistical Differential Privacy in the Client-Server Model ⋮ Predicate encryption from bilinear maps and one-sided probabilistic rank ⋮ Differentially Private Learning of Geometric Concepts ⋮ Practical Order-Revealing Encryption with Limited Leakage ⋮ On Removing Graded Encodings from Functional Encryption ⋮ Decomposable obfuscation: a framework for building applications of obfuscation from polynomial hardness ⋮ The Complexity of Differential Privacy
Uses Software
Cites Work
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- Cryptographic lower bounds for learnability of Boolean functions on the uniform distribution
- Indistinguishability Obfuscation: From Approximate to Exact
- Functional Encryption Without Obfuscation
- Candidate Indistinguishability Obfuscation and Functional Encryption for All Circuits
- Faster Algorithms for Privately Releasing Marginals
- Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
- Multiparty Key Exchange, Efficient Traitor Tracing, and More from Indistinguishability Obfuscation
- Interactive privacy via the median mechanism
- Iterative Constructions and Private Data Release
- Property Preserving Symmetric Encryption
- Semantically Secure Order-Revealing Encryption: Multi-input Functional Encryption Without Obfuscation
- Faster private release of marginals on small databases
- PCPs and the Hardness of Generating Private Synthetic Data
- What Can We Learn Privately?
- Efficient noise-tolerant learning from statistical queries
- Bounds on the Sample Complexity for Private Learning and Private Data Release
- Sample Complexity Bounds on Differentially Private Learning via Communication Complexity
- Our Data, Ourselves: Privacy Via Distributed Noise Generation
- Fully Collusion Resistant Traitor Tracing with Short Ciphertexts and Private Keys
- A theory of the learnable
- Computational limitations on learning from examples
- Cryptographic limitations on learning Boolean formulae and finite automata
- Using Convex Relaxations for Efficiently and Privately Releasing Marginals
- Equivalences and Separations Between Quantum and Classical Learnability
- Foundations of Cryptography
- ML Confidential: Machine Learning on Encrypted Data
- Candidate Multilinear Maps from Ideal Lattices
- On the complexity of differentially private data release
- Order-Preserving Encryption Revisited: Improved Security Analysis and Alternative Solutions
- Fingerprinting codes and the price of approximate differential privacy
- Function-Private Functional Encryption in the Private-Key Setting
- New Techniques for Noninteractive Zero-Knowledge
- Multi-input Functional Encryption
- Answering n {2+o(1)} counting queries with differential privacy is hard
- Theory of Cryptography
- Computational sample complexity and attribute-efficient learning
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