Concept-Aware Privacy Mechanisms for Defending Embedding Inversion Attacks
#Text embeddings #SPARSE framework #Embedding inversion #Differential privacy #NLP security #Machine learning
📌 Key Takeaways
- Researchers developed SPARSE to defend against embedding inversion attacks in NLP.
- The framework addresses the utility loss caused by excessive noise in traditional differential privacy.
- SPARSE uses a concept-specific approach to protect sensitive attributes within text embeddings.
- The mechanism aims to balance high-level data security with the functional accuracy of AI models.
📖 Full Retelling
🏷️ Themes
Cybersecurity, Artificial Intelligence, Data Privacy
📚 Related People & Topics
Differential privacy
Methods of safely sharing general data
Differential privacy (DP) is a mathematically rigorous framework for releasing statistical information about datasets while protecting the privacy of individual data subjects. It enables a data holder to share aggregate patterns of the group while limiting information that is leaked about specific i...
Word embedding
Method in natural language processing
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be simil...
🔗 Entity Intersection Graph
Connections for Differential privacy:
- 🌐 Machine learning (1 shared articles)
📄 Original Source Content
arXiv:2602.07090v1 Announce Type: cross Abstract: Text embeddings enable numerous NLP applications but face severe privacy risks from embedding inversion attacks, which can expose sensitive attributes or reconstruct raw text. Existing differential privacy defenses assume uniform sensitivity across embedding dimensions, leading to excessive noise and degraded utility. We propose SPARSE, a user-centric framework for concept-specific privacy protection in text embeddings. SPARSE combines (1) diffe