Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval
  Systems
Shengyao Zhuang Bevan Koopman Xiaoran Chu Guido Zuccon
The introduction of Vec2Text, a technique for inverting text embeddings, has
raised serious privacy concerns within dense retrieval systems utilizing text
embeddings, including those provided by OpenAI and Cohere. This threat comes
from the ability for a malicious attacker with access to text embeddings to
reconstruct the original text.
  In this paper, we investigate various aspects of embedding models that could
influence the recoverability of text using Vec2Text. Our exploration involves
factors such as distance metrics, pooling functions, bottleneck pre-training,
training with noise addition, embedding quantization, and embedding dimensions
-- aspects not previously addressed in the original Vec2Text paper. Through a
thorough analysis of these factors, our aim is to gain a deeper understanding
of the critical elements impacting the trade-offs between text recoverability
and retrieval effectiveness in dense retrieval systems. This analysis provides
valuable insights for practitioners involved in designing privacy-aware dense
retrieval systems. Additionally, we propose a straightforward fix for embedding
transformation that ensures equal ranking effectiveness while mitigating the
risk of text recoverability.
  Furthermore, we extend the application of Vec2Text to the separate task of
corpus poisoning, where, theoretically, Vec2Text presents a more potent threat
compared to previous attack methods. Notably, Vec2Text does not require access
to the dense retriever's model parameters and can efficiently generate numerous
adversarial passages.
  In summary, this study highlights the potential threat posed by Vec2Text to
existing dense retrieval systems, while also presenting effective methods to
patch and strengthen such systems against such risks.