List-Decodable Byzantine Robust PIR: Lower Communication Complexity, Higher Byzantine Tolerance, Smaller List Size
June 21, 2025 Β· Declared Dead Β· π International Conference on the Theory and Application of Cryptology and Information Security
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Pengzhen Ke, Liang Feng Zhang, Huaxiong Wang, Li-Ping Wang
arXiv ID
2506.17625
Category
cs.CR: Cryptography & Security
Citations
0
Venue
International Conference on the Theory and Application of Cryptology and Information Security
Last Checked
4 months ago
Abstract
Private Information Retrieval (PIR) is a privacy-preserving primitive in cryptography. Significant endeavors have been made to address the variant of PIR concerning the malicious servers. Among those endeavors, list-decodable Byzantine robust PIR schemes may tolerate a majority of malicious responding servers that provide incorrect answers. In this paper, we propose two perfect list-decodable BRPIR schemes. Our schemes are the first ones that can simultaneously handle a majority of malicious responding servers, achieve a communication complexity of $o(n^{1/2})$ for a database of size n, and provide a nontrivial estimation on the list sizes. Compared with the existing solutions, our schemes attain lower communication complexity, higher byzantine tolerance, and smaller list size.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted