Zobrazeno 1 - 10
of 517
pro vyhledávání: '"Ye Qingqing"'
Federated learning is a decentralized machine learning approach where clients train models locally and share model updates to develop a global model. This enables low-resource devices to collaboratively build a high-quality model without requiring di
Externí odkaz:
http://arxiv.org/abs/2412.06157
Adversarial Training (AT) is one of the most effective methods to enhance the robustness of DNNs. However, existing AT methods suffer from an inherent trade-off between adversarial robustness and clean accuracy, which seriously hinders their real-wor
Externí odkaz:
http://arxiv.org/abs/2410.12671
Autor:
Liang, Zi, Ye, Qingqing, Wang, Yanyun, Zhang, Sen, Xiao, Yaxin, Li, Ronghua, Xu, Jianliang, Hu, Haibo
Model extraction attacks (MEAs) on large language models (LLMs) have received increasing research attention lately. Existing attack methods on LLMs inherit the extraction strategies from those designed for deep neural networks (DNNs) yet neglect the
Externí odkaz:
http://arxiv.org/abs/2409.02718
The drastic increase of large language models' (LLMs) parameters has led to a new research direction of fine-tuning-free downstream customization by prompts, i.e., task descriptions. While these prompt-based services (e.g. OpenAI's GPTs) play an impo
Externí odkaz:
http://arxiv.org/abs/2408.02416
Answering range queries in the context of Local Differential Privacy (LDP) is a widely studied problem in Online Analytical Processing (OLAP). Existing LDP solutions all assume a uniform data distribution within each domain partition, which may not a
Externí odkaz:
http://arxiv.org/abs/2407.13532
Autor:
Luo Xiaojuan, Ye Qingqing
Publikováno v:
E3S Web of Conferences, Vol 441, p 02010 (2023)
Agricultural carbon emission reduction is indispensable to the achievement of the "double carbon" goal, and the multidimensional relationship network has become the key to the decision-making of farmers' carbon emission reduction behaviour. Based on
Externí odkaz:
https://doaj.org/article/270f380d37b7456a8ddcc092d5c8f973
Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, l
Externí odkaz:
http://arxiv.org/abs/2404.03873
With the exponential growth of data and its crucial impact on our lives and decision-making, the integrity of data has become a significant concern. Malicious data poisoning attacks, where false values are injected into the data, can disrupt machine
Externí odkaz:
http://arxiv.org/abs/2403.10313
Local differential privacy (LDP), which enables an untrusted server to collect aggregated statistics from distributed users while protecting the privacy of those users, has been widely deployed in practice. However, LDP protocols for frequency estima
Externí odkaz:
http://arxiv.org/abs/2403.09351
Machine learning models are known to memorize private data to reduce their training loss, which can be inadvertently exploited by privacy attacks such as model inversion and membership inference. To protect against these attacks, differential privacy
Externí odkaz:
http://arxiv.org/abs/2311.14056