Javalin Technology Series

Secure your AI Embeddings with Homomorphic Encryption

Sharath Rajasekar
Founder & CEO, Javelin
April, 2025
Nov, 2024

Introduction

In the evolving landscape of artificial intelligence and machine learning, vector embeddings are a fundamental concept at the core of modern algorithms. These mathematical representations transform abstract data—text, images, or categorical labels—into numerical vectors. This critical transformation enables machine learning models to process and understand complex data.

Vector embeddings are especially pervasive in natural language processing (NLP), where words, phrases, or entire documents are converted into vectors of real numbers. These embeddings capture semantic meaning, relationships, and context, allowing AI Applications or AI Agents to perform human-like reasoning over text-based data. For instance, word similarity can be quantitatively assessed based on the ‘distance’ between their corresponding vectors in a multi-dimensional space.

Vector embeddings often contain insights derived from massive datasets, including personal user information, sensitive, confidential information, proprietary corporate data, and more.

Current Challenges

Data Vulnerability

Vector embeddings often derive from sensitive information, such as personal user interactions, confidential business data, or proprietary intellectual property. When these embeddings are stored or processed without encryption, they become vulnerable to unauthorized access and cyberattacks. The implications range from personal data breaches to the theft of competitive business insights.

Privacy Risks

In many applications, vector embeddings can inadvertently reveal information about individuals that should remain private. For example, embeddings used in personalized recommendation systems or predictive typing can potentially expose a user’s preferences, health status, or other personal attributes. Any breach or misuse of these embeddings could lead to significant privacy violations without encryption.

Regulatory Compliance

Global data protection laws, such as the General Data Protection Regulation (GDPR) in Europe, mandate stringent handling and processing of personal data. These laws often require that any data that can be linked back to an individual, directly or indirectly, be adequately protected against misuse and unauthorized access. Non-encrypted embeddings that contain or can reveal personal information might lead to non-compliance and substantial fines.

Adversarial Inversion Attacks

Embedding inversion attacks may be used to decode embeddings back into their source data. We are seeing sophisticated attacks that can extract information about the source data, infer sentence authorship, or even extract training data from the embedding model without knowing anything about the model. Organizations that do not encrypt embeddings may leave a critical gap in their security frameworks.

Intellectual Property Exposure

For businesses, embeddings can encapsulate core components of proprietary algorithms or business intelligence. If competitors access these non-encrypted embeddings, it could result in a loss of competitive advantage and even potential legal challenges if proprietary information is reverse-engineered.

What is Homomorphic Encryption?

Homomorphic cryptography, also known as homomorphic encryption (HE), is a method of encryption that allows users to perform mathematical operations on encrypted data without first decrypting it. Data can be processed encrypted, safeguarding the underlying information throughout the computation process.

How Javelin secures embeddings

One of the key technologies we use at Javelin to deliver robust security is homomorphic encryption (HE), a form of cryptography that enables computation on encrypted data. This technique allows embedding vectors to be encrypted so that operations can still be performed, producing a result that matches those performed on the plaintext vectors. Today, we are thrilled to announce Javelin’s homomorphic encryption techniques, which, combined with our privacy-preserving techniques, are designed to protect Enterprise embeddings at scale.

Applying HE to AI Vector Embeddings

For AI use cases like Retrieval Augmented Generation(RAG), embedding vectors are often stored in Vector Databases like Pinecone, where you can execute similarity matching algorithms like k-nearest neighbors (KNN) or cosine similarity for semantic searching.

To maintain compatibility with the existing ecosystem of Vector databases and AI workflows and to provide a drop-in capability that requires minimal to zero code change in Applications, we had to design an encryption algorithm that allows semantic search algorithms to work transparently.

Implementing these techniques in Production

The process starts with an application querying the embedding model (e.g., Azure OpenAI’s ada text embeddings) to embed a chunk of text. You then transparently “drop-in” Javelin into the loop to ensure embeddings are encrypted… that's it!

The steps below should be highly familiar to developers who have used embeddings or build RAG applications or Chatbots.

Step 1: Initialize your application to use Javelin

Step 2: Embed your text chunks

Javelin will transparently apply HE on encryptions:

Step 3: That's it! No decryption is required!

Your existing code for retrieval and semantic search will not require any change. Just make sure you encrypt both the embeddings you store in the Vector DB and the queries themselves. As you can see, there is no decryption anywhere — meaning that the embeddings are encrypted in transit and at rest in the vector database. All semantic operations continue to work on the encrypted vectors in the vector database.

Encryption Considerations

Javelin offers several configurations to customize how encryption may be handled in an enterprise:

Company-wide Secure Encryption — Javelin ensures that encryption keys are directly mapped to specific customers. Bring your key (BYOK) with centralized key management, which enables you to configure custom keys that can be applied to our homomorphic encryption scheme to secure embeddings.

Application-specific Encryption — you can even configure Javelin to perform application-specific encryption so that the exact text is encrypted using different application-specific keys, yielding different outputs based on the application using the information.

Encryption Considerations

For vector embeddings, homomorphic encryption allows machine learning models to perform necessary computations like distance measurements, clustering, and nearest neighbor searches without accessing the raw, decrypted data. This capability is crucial for applications involving sensitive information, where privacy and security are paramount.

Encryption in Transit, Encryption at Rest: Our HE techniques effectively close the loop on embedding security to ensure the encryption of embeddings in both transit and at rest. This protects enterprises against data exfiltration or man-in-the-middle attacks that seek to extract embeddings that may contain confidential information.

Preserving Privacy: By using homomorphic encryption, the privacy of the data represented by the embeddings is maintained, even during analysis or processing. This is especially important in fields such as healthcare, where patient data confidentiality must be preserved even during predictive analytics.

Enabling Secure Development: Organizations can manage data processing tasks for developers or teams without compromising the security of their data. Since the computations can be performed on the encrypted data, the individual teams do not need access to the raw embeddings, reducing the risk of data breaches.

Privacy, Compliance with Regulations: This method also helps comply with stringent data protection regulations, as the data remains encrypted throughout its lifecycle, ensuring that personal or sensitive data is not exposed during processing.

Ready to try this out?

At Javelin, we are building a cutting-edge security platform for enterprise-scale AI use cases. Building a RAG app? Have feedback? We’d love to hear from you.

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