Vector Search on Encrypted Data

In recent years, vector search has emerged as a powerful technique for delivering better search results by focusing on the semantic meaning of queries, rather than merely relying on exact keywords. By converting text into a vector representation using a pre-trained model, this method allows for more nuanced and context-aware searches. Vector search is commonly employed in search engines, recommendation systems, and various applications where relevance and accuracy are critical.

What is Encrypted Vector Search?

Encrypted vector search allows us to perform a vector-based search on encrypted data. This technique provides a solution for scenarios where sensitive information must be protected. With encrypted vector search, the search engine can provide relevant results without directly accessing the actual data. In this setup, both the query and the database, or sometimes just the query (if the database is public), remain encrypted, ensuring privacy while still allowing accurate search results.

The Role of CoFHE

CoFHE allows secure, privacy-preserving searches to be conducted on encrypted data, making it an ideal solution for applications involving sensitive data.

Using Encrypted Vector Search with CoFHE

The libcofhe library includes a set of APIs for conducting encrypted vector searches using SMPC and homomorphic encryption. Python bindings are available, making it easier for developers to integrate encrypted vector search capabilities into their applications. Below, we walk through a sample implementation.

Implementation Example

The following Python code snippet demonstrates how to perform an encrypted vector search using CoFHE:

import cofhe

# Initialize CoFHE using a configuration file
cofhe.init("path/to/config.json")

# Load a pre-trained model for generating vector representations
model = cofhe.load_model("/path/to/model")

# Load the database of vectors, which might be a reference to a database on the network
database = cofhe.load_database("/path/to/config.json")

# Encrypt the search query
query = input("Enter a query: ")
query_embedding = model.encode(query)
encrypted_query = cofhe.encrypt(query_embedding)

# Perform an encrypted vector search
results = cofhe.vector_search(encrypted_query, database)

# Decrypt and display the results
decrypted_results = cofhe.decrypt(results)
print(decrypted_results)

Step-by-Step Explanation

1. Initialization: CoFHE nodes are initialized using a configuration file. This file determines the system setup, including access to models and the database.

2. Loading the Model: A pre-trained model (any model that can be used for vector search) is loaded. This model can be hosted locally or on the compute node, even in encrypted form, allowing for flexibility depending on security requirements.

3. Loading the Database: A database of vectors is loaded. This database could be either local or a network-based reference, as specified in the configuration file.

4. Encrypting the Query: The user inputs a query, which is then transformed into a vector using the model. This vector representation of the query is encrypted to preserve privacy.

5. Encrypted Vector Search: Using the encrypted query, a vector search is conducted on the database. The search leverages CoFHE protocols, enabling similarity matching without decrypting the data.

6. Decrypting the Results: The search results, which consist of database IDs corresponding to the most similar vectors, are decrypted for display. The user can then use these IDs to fetch the actual data, provided they have the necessary authentication.

How it Works: Privacy-Preserving Mechanisms

In encrypted vector search, similarity is calculated using cosine similarity between the query vector and vectors in the database. This ensures that neither the search engine nor any other third party can view the actual query or data, providing a strong layer of privacy.

The results contain only the database IDs of relevant entries, keeping the actual data separate. When needed, authorized users can use these IDs to retrieve the data, maintaining strict privacy controls.

Applications of Encrypted Vector Search

Encrypted vector search is a game-changer in areas where sensitive data must be kept private but search relevance remains crucial. Some use cases include:

• Healthcare: Encrypted vector search can allow healthcare providers to search medical records without exposing patient data. This ensures compliance with regulations like HIPAA while enabling data-driven insights.

• Finance: Financial institutions can use encrypted vector search to query transaction or investment data, helping clients access relevant information without risking data leaks.

• Recommendation Systems: Whether for e-commerce or media streaming, recommendation systems can employ encrypted vector search to suggest relevant items while protecting user preferences and activity data.

• General Search Engines: Encrypted vector search provides a way to maintain privacy for general search engines, addressing concerns around user data privacy while still offering personalized search results.