Introduces a distributed stochastic setting for graph optimization and supplies fast approximation algorithms for matching, vertex cover, and dominating set that surpass non-stochastic lower bounds.
In: Proceed- ings of the Forty-First Annual ACM Symposium on Theory of Computing
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
verdicts
UNVERDICTED 7representative citing papers
Random orthonormal matrices are minimax optimal for sketched least squares and rotation-invariant embeddings for randomized SVD, yielding the sharpest error bounds.
PD-FHC embeds one real and multiple decoy Boolean computations in RGB images using Fredkin-gate circuits so the cloud processes them uniformly, allowing the user to reveal only a decoy under coercion while hiding the real result.
GPIR achieves up to 297 times higher throughput than prior GPU PIR systems by fusing operations in stages and using pipelined transposed layouts to cut DRAM traffic during batched lattice-based queries.
AEGIS reduces inter-GPU communication by up to 81.3% in self-attention and reaches 96.62% scaling efficiency with 3.86x speedup on four GPUs for 2048-token encrypted Transformer inference.
A threshold CKKS-based federated framework for Kaplan-Meier curves that aggregates encrypted per-time-point counts and matches centralized results while blocking reconstruction attacks.
CPPDD is a new consensus-based protocol for privacy-preserving multi-client data sharing that achieves unanimous-release confidentiality, linear scalability, and high-probability malicious deviation detection.
citing papers explorer
-
Distributed Stochastic Graph Algorithms
Introduces a distributed stochastic setting for graph optimization and supplies fast approximation algorithms for matching, vertex cover, and dominating set that surpass non-stochastic lower bounds.
-
Sharp analysis of sketched least squares and randomized low-rank approximation
Random orthonormal matrices are minimax optimal for sketched least squares and rotation-invariant embeddings for randomized SVD, yielding the sharpest error bounds.
-
Plausible Deniability in Fully Homomorphic Computation
PD-FHC embeds one real and multiple decoy Boolean computations in RGB images using Fredkin-gate circuits so the cloud processes them uniformly, allowing the user to reveal only a decoy under coercion while hiding the real result.
-
GPIR: Enabling Practical Private Information Retrieval with GPUs
GPIR achieves up to 297 times higher throughput than prior GPU PIR systems by fusing operations in stages and using pipelined transposed layouts to cut DRAM traffic during batched lattice-based queries.
-
AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
AEGIS reduces inter-GPU communication by up to 81.3% in self-attention and reaches 96.62% scaling efficiency with 3.86x speedup on four GPUs for 2048-token encrypted Transformer inference.
-
A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis
A threshold CKKS-based federated framework for Kaplan-Meier curves that aggregates encrypted per-time-point counts and matches centralized results while blocking reconstruction attacks.
-
Secure, Verifiable, and Scalable Multi-Client Data Sharing via Consensus-Based Privacy-Preserving Data Distribution
CPPDD is a new consensus-based protocol for privacy-preserving multi-client data sharing that achieves unanimous-release confidentiality, linear scalability, and high-probability malicious deviation detection.