Embedding Vector Permutation Against Inversion Attacks
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Solution Overview
Problem
Conventional methods to protect against embedding inversion attacks, such as encryption and adding Gaussian noise, are inadequate as they either complicate search performance or can be overcome by machine learning models, posing a threat to the security of sensitive information stored in vector databases.
Innovation Solution
A system that applies permutations to embedding vectors based on seeds, storing permuted vectors in a database while maintaining the seeds separately, ensuring effective search and significantly increasing the complexity for attackers to reconstruct original data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If encryption is applied to embedding vectors, then data security is improved, but search performance becomes complicated
Solution Approach 1:
The system separates the protection mechanism into two independent parts: the embedding vectors are permuted using a permutation function, while the permutation seed is stored separately in a key-value store. This segmentation allows search operations to work with permuted vectors without needing to handle the full complexity of decryption, improving search performance while maintaining security.
Solution Approach 2:
The permutation function acts as an intermediary transformation that converts embedding vectors into permuted vectors without requiring full encryption/decryption operations. This intermediary approach provides security through permutation while keeping search operations simpler compared to traditional encryption methods.
2Reliability
If Gaussian noise is added to embedding vectors, then data security is improved, but machine learning models can still overcome this protection
Solution Approach 1:
The system changes the fundamental parameter of protection from adding noise (which preserves vector space relationships) to applying permutation transformations. This parameter change fundamentally alters the vector representation in a way that breaks the mathematical relationships that machine learning models exploit in embedding inversion attacks, providing stronger security guarantees.
Solution Approach 2:
Instead of trying to preserve vector relationships while adding protection (as with Gaussian noise), the system inverts the approach by fundamentally transforming the vector structure through permutation. This inversion of the protection strategy makes it infeasible for attackers to use traditional embedding inversion techniques, as the permuted vectors no longer maintain the original semantic relationships.
3Object-affected harmful factors
If permutation is applied to embedding vectors, then embedding inversion attacks become infeasible, but search effectiveness must be preserved
Solution Approach 1:
The system performs preliminary permutation of embedding vectors before they are stored in the vector database. This preliminary action ensures that when search operations are performed, they work with already-permuted vectors, maintaining search effectiveness while the permutation itself provides protection against embedding inversion attacks. The permutation is applied in advance, so it does not interfere with the search process.
Data Source
AI summary
Methods, systems, and non-transitory computer readable media are configured to perform operations comprising receiving an embedding vector associated with first data; permuting the embedding vector to generate a permuted embedding vector; and providing the permuted embedding vector to a vector database.