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Kui Ren

21 papers hereh-index 14821 citations42 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author17

Across the 18 of 21 papers where every author was matched, so the position is known.

fields
  • cs.CR11
  • cs.CV6
  • cs.LG3
  • cs.DB1
same name
  • Kui Ren — 53 papers, h 23
  • Kui Ren — 33 papers, h 23
  • Kui Ren — 19 papers, h 7
  • Kui Ren — 18 papers, h 5
  • Kui Ren — 9 papers, h 9
  • Kui Ren — 9 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20222026
most citedAction Recognition with Multi-stream Motion Modeling and Mutual Information Maximization

12 citations · 24 across the 20 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CR2024

RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent

Huiyu Xu, Wenhui Zhang, Zhibo Wang +5

Recently, advanced Large Language Models (LLMs) such as GPT-4 have been integrated into many real-world applications like Code Copilot. These applications have significantly expand…

cs.CR2024

Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning

Zhibo Wang, Zhiwei Chang, Jiahui Hu +4

Federated Learning (FL) exhibits privacy vulnerabilities under gradient inversion attacks (GIAs), which can extract private information from individual gradients. To enhance privac…

cs.CR2024★ 1 cited

Textual Unlearning Gives a False Sense of Unlearning

Jiacheng Du, Zhibo Wang, Jie Zhang +3

Language Models (LMs) are prone to ''memorizing'' training data, including substantial sensitive user information. To mitigate privacy risks and safeguard the right to be forgotten…

cs.CR2024★ 1 cited

SoK: On Gradient Leakage in Federated Learning

Jiacheng Du, Jiahui Hu, Zhibo Wang +4

Federated learning (FL) facilitates collaborative model training among multiple clients without raw data exposure. However, recent studies have shown that clients' private training…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.