惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 叶小钗
D
Docker
GbyAI
GbyAI
Y
Y Combinator Blog
Google DeepMind News
Google DeepMind News
G
Google Developers Blog
P
Proofpoint News Feed
云风的 BLOG
云风的 BLOG
雷峰网
雷峰网
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Stack Overflow Blog
Stack Overflow Blog
WordPress大学
WordPress大学
小众软件
小众软件
Engineering at Meta
Engineering at Meta
酷 壳 – CoolShell
酷 壳 – CoolShell
I
InfoQ
B
Blog
H
Help Net Security
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - 聂微东
The GitHub Blog
The GitHub Blog
A
About on SuperTechFans
B
Blog RSS Feed
Microsoft Security Blog
Microsoft Security Blog

cs.IT updates on arXiv.org

Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Improving Privacy in Graphs Through Node Addition
Nazanin Takbiri, Xiaozhe Shao, Lixin Gao, Hossein Pishro-Nik · 2019-10-19 · via cs.IT updates on arXiv.org

The rapid growth of computer systems which generate graph data necessitates employing privacy-preserving mechanisms to protect users' identity. Since structure-based de-anonymization attacks can reveal users' identity's even when the graph is simply anonymized by employing naive ID removal, recently, $k-$anonymity is proposed to secure users' privacy against the structure-based attack. Most of the work ensured graph privacy using fake edges, however, in some applications, edge addition or deletion might cause a significant change to the key property of the graph. Motivated by this fact, in this paper, we introduce a novel method which ensures privacy by adding fake nodes to the graph. First, we present a novel model which provides $k-$anonymity against one of the strongest attacks: seed-based attack. In this attack, the adversary knows the partial mapping between the main graph and the graph which is generated using the privacy-preserving mechanisms. We show that even if the adversary knows the mapping of all of the nodes except one, the last node can still have $k-$anonymity privacy. Then, we turn our attention to the privacy of the graphs generated by inter-domain routing against degree attacks in which the degree sequence of the graph is known to the adversary. To ensure the privacy of networks against this attack, we propose a novel method which tries to add fake nodes in a way that the degree of all nodes have the same expected value.