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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.
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.
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.
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.
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.
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.
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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!
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