Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Shafran, Avital"'
Retrieval-augmented generation (RAG) systems respond to queries by retrieving relevant documents from a knowledge database, then generating an answer by applying an LLM to the retrieved documents. We demonstrate that RAG systems that operate on datab
Externí odkaz:
http://arxiv.org/abs/2406.05870
Model extraction attacks are designed to steal trained models with only query access, as is often provided through APIs that ML-as-a-Service providers offer. Machine Learning (ML) models are expensive to train, in part because data is hard to obtain,
Externí odkaz:
http://arxiv.org/abs/2310.01959
Membership inference attacks (MIA) try to detect if data samples were used to train a neural network model, e.g. to detect copyright abuses. We show that models with higher dimensional input and output are more vulnerable to MIA, and address in more
Externí odkaz:
http://arxiv.org/abs/2102.07762
As neural networks revolutionize many applications, significant privacy conflicts between model users and providers emerge. The cryptography community developed a variety of techniques for secure computation to address such privacy issues. As generic
Externí odkaz:
http://arxiv.org/abs/1911.12322